WO2017163561A1 - Operation support device and wind power system - Google Patents

Operation support device and wind power system Download PDF

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Publication number
WO2017163561A1
WO2017163561A1 PCT/JP2017/001621 JP2017001621W WO2017163561A1 WO 2017163561 A1 WO2017163561 A1 WO 2017163561A1 JP 2017001621 W JP2017001621 W JP 2017001621W WO 2017163561 A1 WO2017163561 A1 WO 2017163561A1
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Prior art keywords
failure
data
failure risk
maintenance
probability
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PCT/JP2017/001621
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French (fr)
Japanese (ja)
Inventor
憲生 竹田
寛 新谷
和夫 武藤
智彬 山下
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株式会社日立製作所
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Publication of WO2017163561A1 publication Critical patent/WO2017163561A1/en

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
    • F03D80/50—Maintenance or repair
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present invention particularly relates to an operation assistance device and a wind power generation system for an arbitrary product.
  • SCADA Supervision Control And Data Data Acquisition
  • CMS State Monitoring
  • SHM Structure Monitoring
  • CMS state monitoring
  • SHM structure monitoring
  • all or part of such control measurement, state monitoring, and structure monitoring are performed, and reliability is evaluated together with wind turbine control to realize stable operation of the wind turbine.
  • Patent Document 1 “a fatigue deterioration schedule in which a cumulative operating time of a windmill and an optimum fatigue deterioration degree of the windmill are associated with each other, a fatigue deterioration calculating means for calculating a current fatigue deterioration degree of the windmill, and the fatigue deterioration Driving a wind turbine comprising: an operation control unit that controls the operation of the wind turbine according to the relationship between the fatigue deterioration level of the wind speed calculated by the calculation unit and the current optimum fatigue deterioration level acquired from the fatigue deterioration schedule.
  • a control device “(Claim 1) discloses an operation control program and a wind turbine.
  • an operation control system for a wind farm having a plurality of wind turbines a remaining life prediction unit that predicts the remaining life of components for each wind turbine, and a power sale under a plurality of output restriction conditions for each wind turbine.
  • a power sales revenue prediction unit that predicts revenue
  • a maintenance cost prediction unit that predicts a maintenance cost under each output restriction condition based on the remaining life of each part for each wind turbine
  • a prediction for each output restriction condition for each wind turbine Output restriction condition selection unit for selecting an output restriction condition for each wind turbine that maximizes the profit obtained from the wind farm based on the power sale revenue and maintenance cost, and each operation command based on the selected output restriction condition.
  • a wind farm operation control system comprising an operation command section for sending to a windmill "(Claim 5) is disclosed.
  • Patent Document 3 “the maximum value obtained by multiplying the failure probability for each destruction phenomenon in the current operating environment of the equipment and each member constituting the plant by a weighting factor for each damage mode determined in advance for each member. And a plant risk value obtained by calculating a maximum value from a numerical value obtained by multiplying a failure probability for each failure phenomenon under an assumed operation condition of each member by a weighting factor for each damage mode.
  • Patent Documents 1 and 2 above the fatigue damage rate and the remaining life are used as the evaluation criteria for reliability.
  • the reliability of which part is determined. It is not disclosed whether plant operation control or maintenance plan should be implemented based on the property (fatigue damage rate and remaining life).
  • Patent Document 3 a numerical value obtained by multiplying a failure probability by a weighting factor is calculated for each of a plurality of members, and the maximum value is used as a plant risk estimated value to operate the plant. It is disclosed whether to operate the plant with attention and to plan maintenance.
  • Patent Document 3 assumes a thermal power plant such as a steam turbine, and is not considered to be used for wind turbines and construction machines that are exposed to severe outdoor environmental changes.
  • an object of the present invention is to enable a product operation and maintenance plan in consideration of highly accurate evaluation values of reliability of a plurality of parts constituting a product.
  • An auxiliary operation device A failure risk assessment department; Maintenance and operation scenario development department, With The failure risk evaluation unit Using the environmental data and operation data input from a plurality of sensors of the target product, and predetermined design data and material data, the destruction probability F (t1) of the target component p at time t1 is calculated, At time t1, the failure risk RS (t1, p) of the component p included in the product is determined for each failure probability F (t1) of the component p at the time t1 and for each predetermined component p when the component p is broken.
  • the maintenance / operation scenario formulation department The failure risk RS (t1, p) of the component p at time t1 sent from the failure risk evaluation unit, and the past from the past sent from the failure risk evaluation unit and stored in the failure risk database to time t1
  • a trend curve of failure risk is generated with a physical quantity x that affects the failure risk selected in advance from the environmental data and operation data input from the target product and time t as variables
  • Based on the failure risk trend curve obtain a predicted value of failure risk that has been advanced for a predetermined time from the present time,
  • the time t and the physical quantity x And a failure risk prediction model with the physical quantity y affecting the failure risk selected from the maintenance data and / or operation data as variables, and predicting the future failure risk of the component
  • a wind power generation system An operation assisting device as described above; A wind power generator as a target product having a plurality of sensors; A wind power generation system is provided.
  • the failure risk assessment / update unit Using the Bayes' theorem based on the probability density function of the life of the target component p breaking and the likelihood calculated from the failure data included in the failure database, the probability density function of the updated life considering the failure data is obtained. Seeking Based on the probability density function of the lifetime after the update, the environment data and operation data input from a plurality of sensors of the target product and the design data and material data determined in advance are used at the time t1 of the target component p.
  • a wind power generation system An operation assisting device as described above; A first wind power generator as a target product having a plurality of sensors; A second wind power generator having a plurality of sensors, the same type as the first wind power generator, or a similar wind power generator; A wind power generation system is provided.
  • FIG. 5 is a PSN diagram necessary for calculating a failure probability by evaluating the remaining life from the stress history of components included in the target product in the operation assistance system according to the first embodiment of the present invention. It is the figure which showed the method of calculating a damage degree from the stress frequency distribution and PSN diagram which arise in the components contained in the object product among the operation assistance systems by the 1st Embodiment of this invention.
  • the figure which showed the procedure from destruction probability calculation to maintenance / operation scenario formulation among the operation assistance systems by the 1st Embodiment of this invention The block diagram which roughly showed the main component of the operation assistance system by the 2nd Embodiment of this invention, the product which provides data to an operation assistance system, the same model machine, a similar machine, and a database.
  • the probability density function of the life is drawn with the equivalent stress amplitude, and it is Bayes
  • the probability density function of the failure life is updated from the equivalent stress amplitude calculation.
  • the probability density function of the failure life of the parts included in the product when the failure probability is calculated based on the probability density function of the failure life of the parts included in the product, the probability density function of the lifetime set in advance is based on Bayesian statistics. The figure which shows the example to update.
  • a failure life when a failure life is expressed by a multivariate probability density function including time and the failure probability is calculated, a preset probability density function is updated based on Bayesian statistics.
  • the figure which shows the example to do The block diagram which roughly showed the main component of the operation assistance system by the 3rd Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and those relationships.
  • Block diagram The block diagram which roughly showed the main component of the operation assistance system by the 5th Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and those relationships.
  • the main components of the maintenance / operation scenario formulation unit and the flow of data exchanged between the elements when creating a trend curve of the degree of damage are schematically shown. Block diagram.
  • the present embodiment includes a plurality of means for solving the above-mentioned problems, but if an example is given, it is an operation assisting system for an arbitrary product, It is the risk of failure of a plurality of parts constituting the product, in addition to information including at least one of environmental data, operation data, design data, material data of the product from the past to the present, the same type machine of the product, similar
  • An arithmetic unit that performs failure risk evaluation and update based on machine failure data information and obtains failure risk estimates for a plurality of the parts that fluctuate when the maintenance / operation plan of the product is changed at present.
  • means for assigning operation of the product and maintenance time for the plurality of parts by referring to each of the estimated failure risk values of the plurality of parts. According to the present embodiment, it is possible to provide a product operation assistance system capable of formulating a highly reliable product operation and maintenance plan and performing stable operation.
  • FIGS. Operation assistance device and wind power generation system
  • a wind power plant is cited as the product 1, but the application of the present invention and / or the present embodiment is not limited to the wind power plant.
  • a product operation / maintenance plan is implemented by grouping multiple parts that make up a product, taking into account highly accurate reliability evaluation values of the multiple parts that make up the product Can be possible.
  • FIG. 1 is a block diagram schematically illustrating main components of a product operation support system according to the present embodiment, a product and database that provide data used in the operation support system, and a relationship between them.
  • An operation assistance system 100 shown in FIG. 1 includes a failure risk evaluation unit 2 and a maintenance / operation scenario formulation unit 3.
  • the maintenance / operation scenario formulation unit 3 includes a failure risk prediction unit 4 that targets a plurality of parts included in a product and predicts a failure risk of the parts that changes when a maintenance / operation plan is changed.
  • the operation assistance system 100 includes an input unit, a display unit, and an output unit to another device.
  • the product 1 is equipped with a number of sensors for measuring the use environment and the operating state.
  • Environmental data and operation data measured by these sensors are sent to the failure risk evaluation unit 2 of the operation assisting system 100 and used for evaluation.
  • the environmental data is data including data related to the environment to which the product is exposed.
  • wind condition data such as wind speed and direction of a windmill is included in the environmental data.
  • sea condition data such as wavelength and wave height is also a category of environmental data.
  • the operation data is data related to the operating state of the product, such as speed, acceleration, rotation speed, and rotation angle.
  • the amount of power generated by a windmill, the rotational speed of a generator, the azimuth angle, the nacelle angle, and the like are categories of operation data.
  • environmental data and operation data are often measured as control measurement (SCADA).
  • SCADA control measurement
  • CMS state monitoring
  • SHM structure monitoring
  • design data and material data are used in the failure risk evaluation unit 2.
  • the design data includes data relating to the product shape such as a product drawing.
  • the material data includes the characteristics of the materials constituting the product and the characteristics of structures such as bolt fastening and welded joints.
  • FIG. 2 is a block diagram schematically showing processing executed by the failure risk evaluation unit 2.
  • the failure risk evaluation unit 2 calculates a failure risk using at least one of environmental data, operation data, design data, and material data.
  • the failure risk RS (t1, p) of a certain part p included in the product is the destruction probability F (t1) of the target part p at the time t1 and the degree of influence C ( From p), it can be calculated by the following equation. Note that the data of the degree of influence C (p) for each part p is stored in the influence degree database 8 in advance.
  • the failure probability F (t1) can be calculated by remaining life evaluation or predictive diagnosis. First, the remaining life evaluation will be described. In order to obtain the failure probability F (t1) in the remaining life evaluation for the fatigue phenomenon caused by the repeated load acting on the product, first, from the past to the current t1 using the environmental data, operation data and design data. Calculate the history of stress that has occurred in the parts until now. Next, a frequency analysis method such as a rain flow method is applied to the stress history to create a stress frequency distribution that organizes how often a certain amount of stress is generated. Then, the fracture probability F (t1) is obtained using the stress frequency distribution and the material data related to the target part.
  • a frequency analysis method such as a rain flow method
  • FIG. 3 is a PSN diagram necessary for calculating the failure probability by the remaining life evaluation from the stress history of the parts included in the target product.
  • the material data used here is preferably the fatigue life curve of FIG. 3 called a PSN diagram, and is stored in the design / material database 5 in this embodiment.
  • the number of repetitions having a fracture probability P% is obtained from the probability density function 20 of fatigue life obtained by carrying out a fatigue test at each stress amplitude on the vertical axis, and these are connected and illustrated. (FIG. 3).
  • FIG. 4 is a diagram showing a method of calculating the damage degree from the stress frequency distribution generated in the parts included in the target product and the PSN diagram.
  • the fracture probability F (t1) is obtained by the following procedure.
  • n 1 , n 2 , and n m are respectively the number of repetitions of stress amplitudes S 1 , S 2 , and S m obtained by frequency analysis of stress history (m is an integer).
  • N 1 , N 2 , and N m are the number of repetitions of fracture at which fatigue failure occurs with a failure probability P% when stress amplitudes S 1 , S 2 , and S m are repeatedly applied.
  • the number of repetitions N (t1) for generating the damage degree D (t1) can be obtained by the following equation.
  • N (t1) D (t1) ⁇ Np (3)
  • Np is the advance number of repetitions of the failure probability P% defined, an input unit (not shown.)
  • S i e.g., average value, intermediate value, close to their values, etc.
  • the fracture probability F (t1) can be obtained from N (t1) and the probability density function 20 of fatigue life by the equation of FIG. That is, if the elapsed time from the start of operation of the product to the present is t1, the fracture probability F (t1) is a value obtained by integrating the density function f (N) from 0 to N (t1).
  • FIG. 5 is a diagram showing another method for calculating the fracture probability using the probability density function of the fracture life.
  • the failure probability F (t1) until the failure can be obtained by directly defining the probability density function f (t1) of the lifetime as shown in FIG. That is, assuming that the elapsed time from the start of operation of the product to the present time is t1, the fracture probability F (t1) is a value obtained by integrating the density function f (t) from 0 to t1.
  • the life T the difference between the current time t1 and the life time T can be considered as the remaining life, so there is a method for directly defining the probability density function of the life in this way. This can be interpreted as a remaining life evaluation.
  • the life probability density function f (t1) data can be stored in advance in an appropriate memory such as the internal memory of the failure risk evaluation unit 2 for each target product. Further, the data of the probability density function f (t1) of the lifetime can be determined in advance using environmental data, operation data, and the like.
  • FIG. 6 is a diagram illustrating a method of selecting a physical quantity related to destruction from measurement data of a product and appropriately converting it to obtain a probability density function of a lifetime.
  • the operation data when the target part is normal and abnormal are arranged in advance, and the current operation data is monitored to determine the failure of the target part.
  • a failure probability F (t1) at the current time t1 can be obtained by capturing the abnormal operation data group as a probability density function of the failure as shown in FIG. 6 and plotting the position of the current operation data in FIG. .
  • a physical quantity related to the destruction of the target part is selected in advance from the operation data at the time of abnormality and a probability density function using the selected physical quantity as a random variable is created, or related to the destruction of the target part as shown in FIG.
  • a physical quantity included in the operation data is converted into a format to be generated, and a probability density function is created using the converted physical quantities x1 ′ and x2 ′ as random variables.
  • the converted physical quantity includes, for example, a spectrum value of a specific frequency among acceleration spectra obtained by performing fast Fourier transform on acceleration data included in driving data.
  • the probability density function may store in advance an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product.
  • the probability density function can be determined in advance using environmental data, operation data, or the like.
  • the influence degree database 8 stores data of the influence degree C (p) for each part p included in the equation (1). If costs such as all or a predetermined range required when an actual part p is broken are used as the degree of influence C (p), the failure risk RS (t1, p) is caused by a failure at the current time t1. It can be considered as an expected value of loss cost such as all or a predetermined range.
  • the cost required when a part is broken includes the cost of the new part itself, the replacement cost of the part, the transportation cost of the part, and the loss cost of the power generation opportunity due to the shutdown of the product.
  • the failure risk RS (t1, p) can be relatively compared between the components, and it can be determined which component should be considered for reliability.
  • the failure risk RS (t1, p) calculated in this way is input to the maintenance / operation scenario formulation unit 3 together with the maintenance / operation data, environmental data, operation data, design data, and material data.
  • FIG. 7 is a block diagram schematically showing processing executed by the maintenance / operation scenario formulation unit.
  • the maintenance / operation scenario formulation unit 3 includes a failure risk prediction unit 4.
  • the failure risk prediction unit 4 includes a failure risk RS (t 1, p) at time t 1 sent from the failure risk evaluation unit 2 and a failure.
  • a plurality of failures at each time tn (n 0, ⁇ 1, ⁇ 2, ⁇ 3,%) From the past to time t1 that have already been sent from the risk evaluation unit 2 and stored in advance in the failure risk database 30
  • the fluctuation trend of the failure risk is analyzed from the risk RS (tn, p), the environmental data, and the operation data.
  • FIG. 8 is a diagram showing an example of a failure risk trend curve created by failure risk trend analysis of the failure risk prediction unit.
  • a failure risk trend curve created by failure risk trend analysis of the failure risk prediction unit.
  • wind tends to be strong in winter, so that depending on the part, a trend curve can be drawn with time on the horizontal axis and failure risk on the vertical axis as shown in FIG.
  • a regular trend curve as shown in FIG. 8 may not be obtained depending on the PSN diagram and probability density function distribution shape used for the calculation of failure probability. Not a few.
  • the fluctuation of the failure risk RS (t1, p) due to the short-term wind turbulence may be greater than or equal to the seasonal fluctuation
  • the failure risk prediction unit 4 selects the following equations (4), (5), etc. according to the product and the parts constituting the product, where t is the time and x is the selected physical quantity.
  • the curve RS is determined.
  • Equations (4) and (5) are the trend curves of failure risk when the time t and the physical quantity x are variables, but the autoregressive moving average model including the physical quantity and error term from the past to the present time, It is also possible to adopt a neural network learned by inputting environmental data and driving data as a trend curve.
  • RS (t, x, p) ⁇ (p) ⁇ t + ⁇ (p) ⁇ x + ⁇ (p) ( ⁇ , ⁇ , and ⁇ are constants, but depend on parts) However, it is not limited to this.
  • the failure risk prediction unit 4 executes the failure risk prediction 11 based on the failure risk trend curve, maintenance / operation data, and the maintenance / operation scenario defined in the maintenance / operation scenario formulation 12.
  • the maintenance / operation data is past data, and includes, for example, information on periodic inspection of products, control change information for making operations different, and information on inspection execution due to failure.
  • the maintenance / operation scenario refers to, for example, a plan such as how to replace which part in what year and month, how to operate, and the like.
  • the maintenance / operation scenario formulation 12 may automatically create a maintenance / operation scenario by a predetermined method, or may manually input a maintenance / operation scenario from an input unit.
  • FIG. 9 is a diagram illustrating an example of risk prediction processing when the maintenance / operation scenario executed by the maintenance / operation scenario formulation unit is different.
  • the periodic inspection and operation method (product control method, etc.) currently employed in the future are continued, the future failure risk (predicted risk) a advanced by a predetermined time ⁇ T from the present time is shown in FIG.
  • forecast risk a it will be along the trend curve so far.
  • the maintenance / operation scenario formulation unit 3 performs the maintenance / operation scenario formulation 12 using the predicted risk. That is, based on the predicted risk value, the maintenance of the product and the change of the operation method are examined manually by the input from the input unit by the maintenance operator or automatically by a predetermined process. For example, when the predicted risk is higher than a predetermined threshold, a more gentle change to the product operation method is proposed in the maintenance / operation scenario formulation 12, and when the predicted risk is lower than the predetermined threshold, it is more severe Product operation methods and frequent maintenance inspections are proposed.
  • the proposed maintenance / operation scenario is sent again to the failure risk prediction 11, and the future risk b or c is predicted as shown in FIG. 9 according to the maintenance / operation scenario.
  • failure risk from maintenance / operation data in addition to physical quantity x (selected from environmental data and operation data) that affects failure risk at time t The physical quantity y that affects the physical quantity y is selected, and the failure risk prediction model g3 or g4 of the following equation using the physical quantities x and y as variables can be used.
  • RS (t + ⁇ T, y, p) g3 (t + ⁇ T, y, p) (6)
  • RS (t + ⁇ T, x, y, p) g4 (t + ⁇ T, x, y, p) ...
  • Equations (6) and (7) are trend curves in which the time t + ⁇ T and the future physical quantities x and y are variables, and the autoregressive moving average model including the physical quantities and error terms from the past going back to a certain time to the time t + ⁇ T
  • a neural network learned by inputting time, environmental data, operation data, and maintenance / operation data can also be used as a prediction model.
  • FIG. 10 is a diagram illustrating an example in which main components of the product are interpreted as parts, arranged and displayed in descending order of predicted value of failure risk, and grouped.
  • the maintenance / operation scenario formulation 12 shown in FIG. 7 is performed in consideration of grouping. For example, a plan may be established such as which group will be maintained after how many years.
  • the failure risk prediction in FIG. 9 can be performed on all parts constituting the product. However, by predicting failure risk only for parts with high failure risk (or prediction risk or future risk), the man-hours required for prediction can be reduced, and maintenance and operation can be efficiently planned. For example, as shown in FIG.
  • the maintenance / operation scenario formulation unit 3 has a failure risk (or a prediction risk or a future risk) together with a component having the maximum prediction value and the maximum value as shown in FIG. Are arranged in descending order of the predicted values and displayed on the display unit.
  • the group and the maintenance time can be assigned according to the upper and lower thresholds for each group so that the failure risk of all parts does not exceed a certain threshold. For example, such an assignment is possible by a genetic algorithm.
  • the maintenance / operation scenario formulation unit 12 of the maintenance / operation scenario formulation unit 3 refers to FIG. 10, which is manually input by the maintenance operator or the like from the input unit or automatically by a predetermined process for each group. Plan maintenance operations such as when to maintain parts.
  • a maintenance / operation scenario such as maintenance operation contents may be manually input from the input unit.
  • the planned maintenance operation content is stored in an appropriate storage unit and / or displayed on the display unit. If the predicted value of the failure risk (predicted risk or future risk, etc.) is arranged according to the magnitude, for example, as shown in FIG. 10, group A, group B, group C are determined according to a plurality of predetermined thresholds.
  • maintenance can be planned by grouping parts into groups. According to such a maintenance plan based on grouping, for example, replacement of parts belonging to group A is performed at the maintenance time that was most recently planned. It is possible to replace parts with high prediction risk or future risk).
  • Such grouping for assigning maintenance times can be performed using a combinational optimization method such as a genetic algorithm or a branch and bound method.
  • FIG. 11 is a diagram in which predicted values of failure risk are calculated for each part belonging to main components of the product, and the parts are arranged and displayed in descending order of the predicted value.
  • FIG. 10 described above interprets the components included in the product as parts, and arranges the relationship between the parts and the predicted value of the failure risk. For example, a file that defines which component is included in which component is stored in advance in an appropriate storage unit, and the maintenance / operation scenario formulation unit 3 displays the component, component, and failure risk by referring to the file. Can be displayed.
  • the predicted value (predicted risk or future risk) of the failure risk for the component it is possible to clarify the component that affects the product.
  • FIG. 10 described above interprets the components included in the product as parts, and arranges the relationship between the parts and the predicted value of the failure risk.
  • a file that defines which component is included in which component is stored in advance in an appropriate storage unit and the maintenance / operation scenario formulation unit 3 displays the component, component, and failure risk by referring to the file. Can be
  • the predicted value of the failure risk can be arranged for each part included in the component.
  • the operation assistance system of this embodiment is provided with the apparatus which arranges and displays the predicted value of a failure risk like FIG.
  • the failure risk evaluation in the failure risk evaluation unit 2 of the operation assistance system 100, the failure risk prediction in the maintenance / operation scenario formulation unit 3, and the maintenance / operation scenario formulation are executed at certain time intervals. This time interval may be the same as or different from the time interval at which the environmental data and operation data are measured.
  • SCADA control measurement
  • environmental data and operational data statistical values are calculated at 10-minute intervals, for example, and the statistical values are PCs or the like. Is stored in the server configured with.
  • the operation assistance system can include components included in the wind power plant.
  • the wind turbine can be operated stably while sufficiently predicting the failure of the wind turbine.
  • FIG. 12 shows a flowchart of the operation assistance system according to the first embodiment.
  • the failure risk evaluation unit 2 calculates the failure probability F (t1) and the failure risk RS (t1, p) at the time t1 of the target component p (S11, S12).
  • the failure risk prediction unit 4 calculates a failure risk trend curve RS (t, x, p) and a future failure risk RS (t + ⁇ T, x, y, p) (S13, S14). Whether maintenance / operation is to be changed automatically based on the calculated future failure risk, for example by comparing with a predetermined threshold, or by manual input from the maintenance / operator etc. Judging.
  • the physical quantity / condition to be changed to the maintenance / operation scenario formulation 12 automatically by a predetermined process or manually by an input from an input unit by a maintenance / operator or the like.
  • the maintenance / operation scenario formulation unit 3 formulates a maintenance / operation scenario according to the setting / input (S15, S16, S14). As described above, such a procedure is repeated at intervals of 10 minutes, for example, to assist the stable operation of the product.
  • the failure risk evaluation / update unit 14 executes failure risk evaluation and update using the failure data information.
  • Failure data of a plurality of parts constituting the product 1 and its similar machines and similar machines 13 is stored in the failure database 15.
  • the failure data includes, for example, the operation time from the operation start to the failure, the environmental data from the operation start to the time of the failure, the operation data, and the maintenance / operation data.
  • the same type machine includes products of the same type as that of the product 1, and the similar machine includes a product of a different type from the product 1.
  • FIG. 14 is a diagram showing an example in which the probability density function of the life is drawn with the equivalent stress amplitude and updated based on Bayesian statistics when the failure probability is calculated by the remaining life evaluation from the stress history of the parts included in the product. is there.
  • the failure risk evaluation / update unit 14 targets fatigue phenomena that occur due to repeated loads on the product and obtains the failure probability F (t1) in the remaining life evaluation, the stress generated in the part from the past to the current t1
  • a stress frequency distribution is obtained by applying a frequency analysis method such as a rainflow method to the history (see FIG. 4).
  • the stress frequency distribution 21 is used to express the stress, but the equivalent stress amplitude when only a certain magnitude of stress is assumed to be generated.
  • Seq can be calculated from the stress frequency distribution using, for example, the formula in FIG. If the equivalent stress amplitude Seq is obtained, the probability density function 16 of the lifetime at which the part breaks at the equivalent stress amplitude Seq can be drawn.
  • FIG. 15 shows a flowchart for updating the density function of such a fracture life.
  • the failure risk evaluation / update unit 14 uses the equation shown in FIG. 14 for the equivalent stress amplitude Seq (p) from the stress frequency distribution obtained by frequency analysis of the stress history from the past to the current time t1 of the target component p. To calculate (S21). Then, the failure risk evaluation / update unit 14 refers to the material data (PSN diagram) of the target part p stored in advance in the design / material database 5 or the like, and uses the equivalent stress amplitude Seq (p). A probability density function f (N) of the fracture life is obtained (S22).
  • the failure risk evaluation / update unit 14 obtains the stress history and stress frequency distribution until failure from the environmental data, operation data, maintenance / operation data and design / material data from the start of operation to the failure of these parts,
  • the fracture life N f j at the equivalent stress amplitude Seq (p j ) is calculated according to the expression on the right side in FIG. 15 (S23).
  • the failure risk evaluation / updating unit 14 can calculate the likelihood L from the k pieces of the fracture life N f j thus obtained according to the expression on the left side in FIG.
  • the failure risk evaluating / updating unit 14 can obtain the updated probability life function f (N) ′ of the fracture life from the probability density function f (N) and the likelihood L in advance according to the equation (8). Yes (S25).
  • the probability density function in advance and the probability distribution shape of likelihood are Weibull distribution or lognormal distribution.
  • the degree of damage D (t1) with respect to fatigue failure P% can be calculated by the following equation.
  • D (t1) (n 1 / N 1 ) + (n 2 / N 2 ) +... + (N m / N m ) ...
  • n 1 , n 2 , and n m are respectively the number of repetitions of stress amplitudes S 1 , S 2 , and S m obtained by frequency analysis of stress history (m is an integer).
  • N 1 , N 2 , and N m are the number of repetitions of fracture at which fatigue failure occurs with a failure probability P% when stress amplitudes S 1 , S 2 , and S m are repeatedly applied.
  • the number of repetitions N (t1) for generating the damage degree D (t1) is obtained by the following equation.
  • N (t1) D (t1) ⁇ Np (3)
  • Np is the number of repetitions with a predetermined failure probability P%, and by setting the stress amplitude to a predetermined stress amplitude S i or Seq (p), as shown in FIG. 4, N (t1) From the probability density function of the fatigue life, the fracture probability F (t1) ′ can be obtained by the equation of FIG.
  • the fracture probability F (t1) ′ is a value obtained by integrating the density function f (N) ′ from 0 to N (t1).
  • the updated probability is updated by using the updated PSN diagram, and the updated probability according to the formula (1) is obtained by multiplying the updated probability F (t1) ′ and the influence degree C (p).
  • the failure risk RS (t1, p) ′ can be calculated.
  • Such a method of updating the failure risk from the stress frequency distribution, PSN diagram, and failure data is an example.
  • the updated failure probability may be calculated without using the equivalent stress amplitude.
  • the update function of the density function of the fracture life based on the Bayes' theorem may be changed according to the use environment of the same type machine as the target product 1, the operating environment of the similar machine 13, the operating situation, the structural similarity, and the like.
  • FIG. 16 is a diagram illustrating an example of updating a preset probability density function of a lifetime based on Bayesian statistics when calculating a fracture probability based on a probability density function of a fracture life of a part included in a product. .
  • the representative Bayes theorem can be used. That is, the lifetime density function and the failure data are substituted into the equation (8) to update the lifetime density function, and the probability probability function after the update is used to determine the fracture probability F (t1) ′ (FIG. 16), which has an effect on it.
  • the failure risk can be updated by multiplying the degree.
  • the breakdown life of the parts of the product 1 and the parts of the same type machine and similar machine 13 will be greatly different even if the operating time is the same. It is done.
  • a physical quantity that affects the fracture life is selected from environmental data and operation data as variables, and a multi-variable life probability density function is created.
  • the data of the probability density function f (t1) of the lifetime can be stored in advance in an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product. Further, the data of the probability density function f (t1) of the lifetime can be determined in advance using environmental data, operation data, and the like.
  • FIG. 17 is a diagram illustrating an example in which the failure life is expressed by a multivariate probability density function including time and the probability density function set in advance is updated based on Bayesian statistics when the failure probability is calculated.
  • FIG. 17 shows an example of a probability density function of a bivariate lifetime.
  • a value x ′ obtained by converting a physical quantity x selected from environmental data or operation data is used as a random variable with time. This conversion includes conversion of a physical quantity that changes over time into a statistical quantity (average value, maximum value, etc. at a certain time interval), conversion into an equivalent physical quantity based on frequency analysis, and the like.
  • the life density function can be updated according to Bayes' theorem as in the case of univariate, as shown in FIG.
  • a physical quantity that greatly affects the fracture life or a function r (t, x ') including a quantity x ′ and a time t converted from the physical quantity is generated, and the probability density of the fracture life is interpreted by interpreting the function as a random variable.
  • the failure risk evaluation / update unit 14 calculates the failure probability by predictive diagnosis
  • the failure probability density function may be updated using the Bayes' theorem represented by the equation (8).
  • a failure probability is obtained from the updated probability density, and the failure risk may be updated by multiplying it by the degree of influence.
  • the probability density function of the bivariate lifetime can store, for example, an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product.
  • the probability density function of the bivariate lifetime can be determined in advance using environmental data, operation data, or the like.
  • failure risk evaluation / update unit 14 and the maintenance / operation scenario formulation unit 3 use the updated failure risk RS (t1, p), as in the first embodiment shown in FIG.
  • Each process of trend analysis (S13), future risk prediction (S14), maintenance / operation change (S15), and maintenance / operation scenario formulation (S16) is executed.
  • the calculation of the failure probability can be made highly accurate, so that the failure risk evaluation and prediction of the parts included in the product 1 Can be carried out with higher accuracy.
  • the time interval of failure risk update may be the same as or different from the failure risk evaluation interval.
  • FIG. 18 is a block diagram schematically showing main components of the operation assistance system according to the third embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationship.
  • the product 1 Information from the external database 17 that does not depend on is used.
  • the external data included in the external database 17 includes, for example, weather calculated by a large computer, sea state future prediction data, resource supply prediction data, resource reserve prediction data, and the like. Such external data is not affected by the operating state of the product 1, and therefore the external data does not depend on the product 1.
  • FIG. 19 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit of the operation assistance system according to the third embodiment of the present invention and the flow of data exchanged between the components.
  • the failure risk prediction 11 of the maintenance / operation scenario formulation unit 3 calculates a future failure risk using the failure risk trend curve, the maintenance / operation scenario, and external data. That is, the predicted value of failure risk can be calculated from the physical quantity x included in the environmental data or the operation data, the physical quantity y included in the maintenance / operation data, and the physical quantity z included in the external data by the following equation.
  • FIG. 20 is a block diagram schematically showing main components of an operation assistance system according to the fourth embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationships.
  • FIG. 21 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit of the operation assistance system according to the fourth embodiment of the present invention and the flow of data exchanged between the components.
  • the environmental data and operation data measured by the product 1 are input to the failure probability evaluation unit 18, where the failure probability F of the parts included in the product 1 is calculated.
  • the calculated failure probability F becomes an input to the maintenance / operation scenario formulation unit 3, where a failure risk trend curve is created by multiplying the failure probability and the influence degree.
  • FIG. 22 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit and the flow of data exchanged between the elements when creating a trend curve of the failure probability. Therefore, in the present embodiment, as shown in FIG. 21, the impact database 8 is arranged in the maintenance / operation scenario formulation unit 3. On the other hand, as shown in FIG. 22, a form of creating a trend curve of failure probability instead of a trend curve of failure risk is also conceivable. In such a case, a trend curve of the failure probability F as shown in the following equation can be created for the part p from the time t and the physical quantity x that affects the failure probability or the failure probability.
  • F (t + ⁇ T, y, p) h3 (t + ⁇ T, y, p) (13)
  • F (t + ⁇ T, x, y, p) h4 (t + ⁇ T, x, y, p) (14)
  • y is a physical quantity that affects the failure risk RS in the maintenance / operation data.
  • the future failure risk RS can be determined by multiplying this future failure probability or failure probability by the degree of influence C (p) as shown in the following equation.
  • FIG. 23 is a block diagram schematically showing main components of an operation assistance system according to the fifth embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationships.
  • the operation assistance system by 5th Embodiment of this embodiment is demonstrated using FIG.
  • environmental data and operation data measured by the product 1 are input to the damage degree evaluation unit 19 where the damage degree of the parts included in the product 1 is calculated.
  • the degree of damage can be obtained by selecting a fatigue life curve with a certain probability of failure in the PSN diagram of FIG. it can.
  • the calculated damage level is input to the maintenance / operation scenario formulation unit 3, where the failure probability is calculated by referring to the material data, and a failure risk trend curve is created by multiplying the damage probability. Therefore, in the present embodiment, as shown in FIG. 21, the impact database 8 is arranged in the maintenance / operation scenario formulation unit 3. On the other hand, as shown in FIG. 22, not only the failure risk trend curve but also a failure probability trend curve may be considered.
  • FIG. 24 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit when creating a trend curve of the degree of damage and the flow of data exchanged between the elements.
  • a form of creating a trend curve of the degree of damage is also conceivable. That is, the failure risk trend curve can also be determined as shown in the following equation by creating the future damage levels d3 and d4 and multiplying by the influence level C (p).
  • K (p) is a conversion constant necessary for calculating the fracture probability from the degree of damage.
  • K (p) may be stored in a damage degree database, an influence degree database, or another database.
  • the failure probability and the damage degree are independently calculated once, there is an advantage that the failure probability and the damage degree can be easily visualized (displayed by the display unit).
  • the failure risk calculated according to the equation (1) is a failure risk that considers all damage accumulated in the product during the period from the start of operation of the product to time t1.
  • a failure risk considering only the damage accumulated in the product and use it as a predicted value of the failure risk.
  • a probability P that a certain part included in the product is broken is calculated by the following equation during a period from the current time t1 to a certain future time point t1 + ⁇ T.
  • F (t1, t1 + ⁇ T) (F (t1 + ⁇ T) ⁇ F (t1)) / (1 ⁇ F (t1)) ... (21)
  • F (t1) and F (t1 + ⁇ T) are destruction probabilities at times t1 and t1 + ⁇ T, respectively.
  • the failure risk during the period from the current time t1 to a future time point t1 + ⁇ T can be calculated from the influence degree C (p) and the failure probability P (t1, t1 + ⁇ T) of the target component by the following equation.
  • Each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor.
  • Information such as programs, tables, and files for realizing each function can be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.
  • the control lines and information lines indicate what is considered necessary for the explanation, and not all the control lines and information lines on the product are necessarily shown. Actually, it may be considered that almost all the components are connected to each other.

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Abstract

The purpose of the present invention is to provide an operation support system which is capable of referring to high-precision evaluation values of reliability of a plurality of components which configure a product, and of carrying out a management and maintenance plan of the product. Provided is an operation support device 100 of an arbitrary product, comprising a fault risk evaluation unit 2 which: derives a fault risk of a plurality of components which configure the product, said fault risk being computed on the basis of information which includes at least one of environmental data, working data, design data, and/or materials data, of the product from the past to the present; and derives fault risk estimation values of the plurality of components which fluctuate if a current management and maintenance plan of the product is varied. The operation support device 100 further comprises a maintenance/management scenario creation unit 3 which is for referring to each of the computed fault risk estimation values of the plurality of components, and assigning the management of the product and maintenance times of the plurality of components.

Description

稼働補助装置及び風力発電システムOperation assistance device and wind power generation system
　本発明は、特に、任意の製品の稼働補助装置及び風力発電システムに関する。
  The present invention particularly relates to an operation assistance device and a wind power generation system for an arbitrary product.
　プラントなどの製品には多数のセンサが実装されており、センサで計測された情報は、安定稼働のための制御、製品の構成要素の信頼性分析（故障予兆診断、余寿命診断など）に基づく保守計画の立案などに利用されている。例えば、発電プラントでは多種多様なセンサで主に３種類の計測が実施されている。その３種類の計測はそれぞれ、制御計測（ＳＣＡＤＡ，Ｓｕｐｅｒｖｉｓｏｒｙ　Ｃｏｎｔｒｏｌ　Ａｎｄ　Ｄａｔａ　Ａｑｕｉｓｉｔｉｏｎ）、状態監視（ＣＭＳ、Ｃｏｎｄｉｔｉｏｎ　Ｍｏｎｉｔｏｒｉｎｇ　Ｓｙｓｔｅｍ）および構造物監視（ＳＨＭ、Ｓｔｒｕｃｔｕｒａｌ　Ｈｅａｌｔｈ　Ｍｏｎｉｔｏｒｉｎｇ）と呼ばれている。風力発電プラントの場合、制御計測（ＳＣＡＤＡ）では、風車の環境条件や稼働状態を把握して適切に風車を制御することを目的として、風速、風向、発電量、発電機の回転数、温度など様々な物理量が計測されている。状態監視（ＣＭＳ）では、風車の故障の予兆を検知し、故障による被害を最小限に抑えることを目的として計測が行われる。また構造物監視（ＳＨＭ）では、風車のブレード等の健全性を評価することを目的として、構造物のひずみなどが計測される。一般的な風車では、このような制御計測、状態監視および構造物監視の全て、あるいは一部を実施して、風車の制御とともに信頼性を評価し、風車の安定運転を実現している。 A lot of sensors are mounted on products such as plants, and the information measured by the sensors is based on control for stable operation and reliability analysis of product components (predictive failure diagnosis, remaining life diagnosis, etc.) It is used for planning maintenance plans. For example, in a power plant, three types of measurement are mainly performed by various sensors. These three types of measurement are called control measurement (SCADA, Supervision Control And Data Data Acquisition), state monitoring (CMS, Condition Monitoring System) and structure monitoring (SHM, Structural Health). In the case of a wind power plant, control measurement (SCADA) is used to understand the environmental conditions and operating conditions of the wind turbine and control the wind turbine appropriately, so that the wind speed, wind direction, power generation, generator speed, temperature, etc. Various physical quantities are measured. In state monitoring (CMS), measurement is performed for the purpose of detecting a sign of a windmill failure and minimizing damage caused by the failure. In the structure monitoring (SHM), the distortion of the structure is measured for the purpose of evaluating the soundness of the blades of the windmill and the like. In general wind turbines, all or part of such control measurement, state monitoring, and structure monitoring are performed, and reliability is evaluated together with wind turbine control to realize stable operation of the wind turbine.
　特許文献１では、「風車の累積運転時間と前記風車の最適疲労劣化度とが対応付けられた疲労劣化スケジュールと、前記風車の現在の疲労劣化度を演算する疲労劣化演算手段と、前記疲労劣化演算手段により演算された前記風速の疲労劣化度と前記疲労劣化スケジュールから取得した現在の最適疲労劣化度との関係に応じて、前記風車の運転を制御する運転制御手段とを具備する風車の運転制御装置」（請求項１）運転制御プログラムおよび風車を開示している。
　特許文献２では、「複数の風車を備えたウィンドファームの運転制御システムであって、各風車について部品の余寿命を予測する余寿命予測部と、各風車について複数の出力制限条件下における売電収入を予測する売電収入予測部と、各風車について前記部品の余寿命に基づいて各出力制限条件下におけるメンテナンスコストを予測するメンテナンスコスト予測部と、各風車について前記出力制限条件ごとに予測された売電収入及びメンテナンスコストに基づいて、ウィンドファームから得られる収益が最大になる出力制限条件を各風車について選択する出力制限条件選択部と、選択された出力制限条件に基づいて運転指令を各風車に送る運転司令部を備えることを特徴とするウィンドファームの運転制御システム」（請求項５）が開示されている。
　特許文献３では、「プラントを構成する機器および個々の部材の現運転環境下での個々の破壊現象に対する破壊確率に、該部材毎に予め決められた損傷形態毎の重み係数を乗算した最大値をプラントリスク推定値とする手段と、前記各部材の想定運転条件下での個々の破壊現象に対する破壊確率に、前記損傷形態毎の重み係数を乗算した数値から最大値を求めたプラントリスク値が、設定値を超えない運用条件を計算してプラント運転制限値を求める手段と、前記部材の現時点の余寿命評価情報から運用計画に基づいて今後の寿命消費を計算する手段と、現時点の余寿命情報と、運用計画に基づき計算した将来の余寿命予測を基に運用計画毎の破壊確率の推移を評価し、前記各部材の損傷形態毎の重み係数を前記破壊確率の推移データに乗算してプラント運用リスク推定値を算出する手段を備えた」（要約）プラント機器の運用診断装置を開示している。
  In Patent Document 1, “a fatigue deterioration schedule in which a cumulative operating time of a windmill and an optimum fatigue deterioration degree of the windmill are associated with each other, a fatigue deterioration calculating means for calculating a current fatigue deterioration degree of the windmill, and the fatigue deterioration Driving a wind turbine comprising: an operation control unit that controls the operation of the wind turbine according to the relationship between the fatigue deterioration level of the wind speed calculated by the calculation unit and the current optimum fatigue deterioration level acquired from the fatigue deterioration schedule. A control device "(Claim 1) discloses an operation control program and a wind turbine.
In Patent Document 2, “an operation control system for a wind farm having a plurality of wind turbines, a remaining life prediction unit that predicts the remaining life of components for each wind turbine, and a power sale under a plurality of output restriction conditions for each wind turbine. A power sales revenue prediction unit that predicts revenue, a maintenance cost prediction unit that predicts a maintenance cost under each output restriction condition based on the remaining life of each part for each wind turbine, and a prediction for each output restriction condition for each wind turbine Output restriction condition selection unit for selecting an output restriction condition for each wind turbine that maximizes the profit obtained from the wind farm based on the power sale revenue and maintenance cost, and each operation command based on the selected output restriction condition. A wind farm operation control system comprising an operation command section for sending to a windmill "(Claim 5) is disclosed. There.
In Patent Document 3, “the maximum value obtained by multiplying the failure probability for each destruction phenomenon in the current operating environment of the equipment and each member constituting the plant by a weighting factor for each damage mode determined in advance for each member. And a plant risk value obtained by calculating a maximum value from a numerical value obtained by multiplying a failure probability for each failure phenomenon under an assumed operation condition of each member by a weighting factor for each damage mode. Means for calculating operation conditions that do not exceed the set value to obtain a plant operation limit value; means for calculating future life consumption based on the operation plan from the current remaining life evaluation information of the member; and current remaining life Evaluate the transition of failure probability for each operation plan based on the information and future remaining life prediction calculated based on the operation plan, and multiply the failure probability transition data by the weighting factor for each damage form of each member It discloses an operational diagnostic device of "Summary plant equipment comprising a means for calculating the plant operational risk estimation value each.
特開２００６－２４１９８１号公報JP 2006-241981 特開２０１３－１７０５０７号公報JP 2013-170507 A 特開２００２－７３１５５号公報JP 2002-73155 A
　上記の特許文献１、２では、疲労損傷率と余寿命を信頼性の評価基準としているが、これら文献では、複数の部品で疲労損傷率や余寿命が得られた場合に、どの部品の信頼性（疲労損傷率や余寿命）に基づいてプラントの運転制御や保守計画を実施すれば良いかが開示されていない。一方、特許文献３では、複数ある部材のそれぞれに対して、破壊確率に重み係数を乗算した数値を計算し、その最大値をプラントリスク推定値としてプラントを運用するため、どの部品の信頼性に着目してプラントを運用し、保守を計画すれば良いかが開示されている。ただし、プラントの保守計画では、リスク値が最大値となる部品だけでなく、保守すべき他の部品のリスク値も考慮して保守計画を立案する必要がある。しかし、上記のいずれの特許文献でも、複数の部品の信頼性評価値（リスク値など）を考慮して運用、保守計画を立案し、プラントを高効率かつ安定に稼働させる手段については開示も示唆もなされていない。さらに、特許文献３は、蒸気タービンといった火力プラントを想定しており、屋外の厳しい環境変化に曝される風車、建設機械等で使用されるものを対象にしていないと考えられる。 In Patent Documents 1 and 2 above, the fatigue damage rate and the remaining life are used as the evaluation criteria for reliability. However, in these documents, when the fatigue damage rate and the remaining life are obtained with a plurality of parts, the reliability of which part is determined. It is not disclosed whether plant operation control or maintenance plan should be implemented based on the property (fatigue damage rate and remaining life). On the other hand, in Patent Document 3, a numerical value obtained by multiplying a failure probability by a weighting factor is calculated for each of a plurality of members, and the maximum value is used as a plant risk estimated value to operate the plant. It is disclosed whether to operate the plant with attention and to plan maintenance. However, in a plant maintenance plan, it is necessary to formulate a maintenance plan in consideration of not only the parts having the maximum risk value but also the risk values of other parts to be maintained. However, in any of the above patent documents, an operation and maintenance plan is made in consideration of reliability evaluation values (risk values, etc.) of a plurality of parts, and disclosure is also suggested for means for operating the plant with high efficiency and stability. It has not been done. Further, Patent Document 3 assumes a thermal power plant such as a steam turbine, and is not considered to be used for wind turbines and construction machines that are exposed to severe outdoor environmental changes.
　また、疲労損傷率、余寿命、破壊確率、リスク値などによる信頼性評価は、特に対象製品が新たに開発された製品の場合、故障データが乏しいことなどが原因で、信頼性の評価精度が必ずしも良いとは限らないという課題がある。上記いずれの特許文献も、対象製品のデータから信頼性を評価し、それを保守や運用に利用することを開示しており、これに対して、例えば、対象製品と同型の製品、類似の製品の信頼性の評価値を使って、対象製品の信頼性評価値を更新し、高精度化した評価値に基づいて製品を運用、保守を計画するものについては開示も示唆もなされていない。 In addition, reliability evaluation based on fatigue damage rate, remaining life, failure probability, risk value, etc., especially for newly developed products, the reliability evaluation accuracy is low due to the lack of failure data. There is a problem that it is not always good. Any of the above patent documents discloses that the reliability is evaluated from the data of the target product and used for maintenance and operation. On the other hand, for example, the same type of product as the target product, similar products There is no disclosure or suggestion of updating the reliability evaluation value of the target product using the reliability evaluation value of the product and planning the operation and maintenance of the product based on the highly accurate evaluation value.
　本発明は、以上の点に鑑み、製品を構成する複数部品の高精度な信頼性の評価値を考慮して、製品の運用、保守計画を実施可能とすることを目的とする。
  In view of the above, an object of the present invention is to enable a product operation and maintenance plan in consideration of highly accurate evaluation values of reliability of a plurality of parts constituting a product.
　本発明の第１の解決手段によると、
　稼働補助装置であって、
　故障リスク評価部と、
　保守・運用シナリオ策定部と、
を備え、
 
前記故障リスク評価部は、
　対象製品の複数のセンサから入力された環境データ及び運転データと、予め定められた設計データ及び材料データを用いて、対象とする部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）を演算し、
　時刻ｔ１において、製品に含まれる部品ｐの故障リスクＲＳ（ｔ１，ｐ）を、部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）と、部品ｐが壊れた場合の予め定められた部品ｐ毎の影響度Ｃ（ｐ）との積により計算し、
 
前記保守・運用シナリオ策定部は、
　前記故障リスク評価部から送られてきた時刻ｔ１での部品ｐの故障リスクＲＳ（ｔ１、ｐ）と、前記故障リスク評価部から既に送られて故障リスクデータベースに記憶された過去から時刻ｔ１までの複数の故障リスクに基づき、対象製品から入力された環境データ及び運転データから予め選択された故障リスクに影響を及ぼす物理量ｘと、時間ｔとを変数として故障リスクのトレンドカーブを生成し、
　故障リスクのトレンドカーブに基づき、現時点から予め定められた時間進んだ故障リスクの予測値を求め、
　故障リスクの予測値に基づき、保守・運用者による入力部からの入力によりマニュアルで設定された又は予め定められた処理により自動で設定された製品の保守・運用シナリオに従い、時刻ｔと、物理量ｘと、保守データ及び／又は運転データから選択された故障リスクに影響を及ぼす物理量ｙとを変数とする故障リスク予測モデルを生成し、部品ｐの将来の故障リスクを予測し、
　部品毎の将来の故障リスクを、故障リスクの予測値の高い順に整理して表示部に表示し及び／又は記憶部に記憶し、予め定められた複数の閾値でグループ分けし、保守・運用者による入力部からの入力によりマニュアルで又は予め定められた処理により自動で、グループ毎に各部品の保守時期を含む保守運用内容を設定して、保守運用内容を記憶部に記憶する及び／又は表示部に表示する、
稼働補助装置が提供される。 According to the first solution of the present invention,
An auxiliary operation device,
A failure risk assessment department;
Maintenance and operation scenario development department,
With

The failure risk evaluation unit
Using the environmental data and operation data input from a plurality of sensors of the target product, and predetermined design data and material data, the destruction probability F (t1) of the target component p at time t1 is calculated,
At time t1, the failure risk RS (t1, p) of the component p included in the product is determined for each failure probability F (t1) of the component p at the time t1 and for each predetermined component p when the component p is broken. Calculated by the product of the degree of influence C (p)

The maintenance / operation scenario formulation department
The failure risk RS (t1, p) of the component p at time t1 sent from the failure risk evaluation unit, and the past from the past sent from the failure risk evaluation unit and stored in the failure risk database to time t1 Based on a plurality of failure risks, a trend curve of failure risk is generated with a physical quantity x that affects the failure risk selected in advance from the environmental data and operation data input from the target product and time t as variables,
Based on the failure risk trend curve, obtain a predicted value of failure risk that has been advanced for a predetermined time from the present time,
Based on the predicted value of failure risk, according to the product maintenance / operation scenario set manually by input from the input unit by the maintenance / operator or automatically set by a predetermined process, the time t and the physical quantity x And a failure risk prediction model with the physical quantity y affecting the failure risk selected from the maintenance data and / or operation data as variables, and predicting the future failure risk of the component p,
The future failure risk for each part is arranged in order of the predicted value of failure risk, displayed on the display unit and / or stored in the storage unit, and grouped by a plurality of predetermined thresholds. The maintenance operation content including the maintenance time of each part is set for each group manually by input from the input unit or automatically by a predetermined process, and the maintenance operation content is stored in the storage unit and / or displayed. To display
An operation assistance device is provided.
　本発明の第２の解決手段によると、
　風力発電システムであって、
　上述のような稼働補助装置と、
　複数のセンサを有する、対象製品である風力発電機と、
を備えた風力発電システムが提供される。 According to the second solution of the present invention,
A wind power generation system,
An operation assisting device as described above;
A wind power generator as a target product having a plurality of sensors;
A wind power generation system is provided.
　本発明の第３の解決手段によると、
　稼働補助装置であって、
　故障リスク評価・更新部と、
　保守・運用シナリオ策定部と、
　対象製品とその同型機及び／又は類似機を構成する複数の部品の故障データを蓄積する故障データベースと、
を備え、
 
前記故障リスク評価・更新部は、
　対象部品ｐが破壊する寿命の確率密度関数と、前記故障データベースに含まれる故障データから計算した尤度とからベイズの定理を活用して、故障データを考慮した更新後の寿命の確率密度関数を求め、
　更新後の寿命の確率密度関数により、対象製品の複数のセンサから入力された環境データ及び運転データと、予め定められた設計データ及び材料データを用いて、対象とする部品ｐの時刻ｔ１での更新後の破壊確率Ｆ（ｔ１）’を演算し、
　時刻ｔ１において、製品に含まれる部品ｐの更新後の故障リスクＲＳ（ｔ１，ｐ）’を、部品ｐの時刻ｔ１での更新後の破壊確率Ｆ（ｔ１）’と、部品ｐが壊れた場合の予め定められた部品ｐ毎の影響度Ｃ（ｐ）との積により計算し、
 
前記保守・運用シナリオ策定部は、
　前記故障リスク評価・更新部から送られてきた時刻ｔ１での部品ｐの更新後の故障リスクＲＳ（ｔ１、ｐ）’と、前記故障リスク評価・更新部から既に送られて故障リスクデータベースに記憶された過去から時刻ｔ１までの複数の更新後の故障リスクに基づき、対象製品から入力された環境データ及び運転データから予め選択された故障リスクに影響を及ぼす物理量ｘと、時間ｔとを変数として故障リスクのトレンドカーブを生成し、
　故障リスクのトレンドカーブに基づき、現時点から予め定められた時間進んだ故障リスクの予測値を求め、
　故障リスクの予測値に基づき、保守・運用者による入力部からの入力によりマニュアルで設定された又は予め定められた処理により自動で設定された製品の保守・運用シナリオに従い、時刻ｔと、物理量ｘと、保守データ及び／又は運転データから選択された故障リスクに影響を及ぼす物理量ｙとを変数とする故障リスク予測モデルを生成し、部品ｐの将来の故障リスクを予測し、予測値をトレンドカーブと共に記憶部に記憶する及び／又は表示部に表示する、
稼働補助装置が提供される。 According to the third solution of the present invention,
An auxiliary operation device,
Failure risk assessment / update department,
Maintenance and operation scenario development department,
A failure database for storing failure data of a plurality of parts constituting the target product and the same type machine and / or similar machine;
With

The failure risk assessment / update unit
Using the Bayes' theorem based on the probability density function of the life of the target component p breaking and the likelihood calculated from the failure data included in the failure database, the probability density function of the updated life considering the failure data is obtained. Seeking
Based on the probability density function of the lifetime after the update, the environment data and operation data input from a plurality of sensors of the target product and the design data and material data determined in advance are used at the time t1 of the target component p. Calculate the updated fracture probability F (t1) ′,
When the failure risk RS (t1, p) ′ after the update of the part p included in the product is updated at the time t1, the failure probability F (t1) ′ after the update at the time t1 of the part p, and the part p is broken Calculated by the product of the degree of influence C (p) for each predetermined part p,

The maintenance / operation scenario formulation department
The failure risk RS (t1, p) ′ after the update of the component p at time t1 sent from the failure risk evaluation / update unit, and already sent from the failure risk evaluation / update unit and stored in the failure risk database Based on a plurality of updated failure risks from the past to time t1, the physical quantity x that affects the failure risk selected in advance from the environmental data and operation data input from the target product and the time t as variables Generate a trend curve of failure risk,
Based on the failure risk trend curve, obtain a predicted value of failure risk that has been advanced for a predetermined time from the present time,
Based on the predicted value of failure risk, according to the product maintenance / operation scenario set manually by input from the input unit by the maintenance / operator or automatically set by a predetermined process, the time t and the physical quantity x And a failure risk prediction model using the physical quantity y that affects the failure risk selected from the maintenance data and / or operation data as variables, predict the future failure risk of the component p, and use the predicted value as a trend curve. And store it in the storage unit and / or display it on the display unit,
An operation assistance device is provided.
　本発明の第４の解決手段によると、
　風力発電システムであって、
　上述のような稼働補助装置と、
　複数のセンサを有する、対象製品である第１の風力発電機と、
　複数のセンサを有し、前記第１の風力発電機と同型機又は類似機の第２の風力発電機と、
を備えた風力発電システムが提供される。
  According to the fourth solution of the present invention,
A wind power generation system,
An operation assisting device as described above;
A first wind power generator as a target product having a plurality of sensors;
A second wind power generator having a plurality of sensors, the same type as the first wind power generator, or a similar wind power generator;
A wind power generation system is provided.
　本発明によれば、製品を構成する複数部品の高精度な信頼性の評価値を考慮して、製品の運用、保守計画を実施可能とすることができる。
  According to the present invention, it is possible to implement a product operation and maintenance plan in consideration of highly accurate evaluation values of reliability of a plurality of parts constituting the product.
本発明の第１の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図。The main component of the operation assistance system by the 1st Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and the block diagram which showed those relationships roughly. 本発明の第１の実施形態による稼働補助システムのうち、故障リスク評価部の主な機能を展開して示したブロック図。The block diagram which expanded and showed the main functions of the failure risk evaluation part among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、対象製品に含まれる部品の応力履歴から余寿命評価で破壊確率を演算するのに必要なＰ－Ｓ－Ｎ線図。FIG. 5 is a PSN diagram necessary for calculating a failure probability by evaluating the remaining life from the stress history of components included in the target product in the operation assistance system according to the first embodiment of the present invention. 本発明の第１の実施形態による稼働補助システムのうち、対象製品に含まれる部品に生じる応力頻度分布とＰ－Ｓ－Ｎ線図から損傷度を演算する方法を示した図である。It is the figure which showed the method of calculating a damage degree from the stress frequency distribution and PSN diagram which arise in the components contained in the object product among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、破壊寿命の確率密度関数を利用して破壊確率を演算する方法を示した図。The figure which showed the method of calculating a failure probability using the probability density function of a failure life among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、製品の計測データから破壊に関連する物理量を選択し、それを適切に変換して寿命の確率密度関数を求める方法を示した図。The figure which showed the method of selecting the physical quantity relevant to destruction from the measurement data of a product among the operation assistance systems by the 1st Embodiment of this invention, and calculating | requiring it appropriately and calculating | requiring the probability density function of a lifetime. 本発明の第１の実施形態による稼働補助システムのうち、保守・運用シナリオ策定部の主な機能を展開して示したブロック図。The block diagram which expanded and showed the main function of the maintenance and operation scenario formulation part among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、故障リスク予測部の故障リスクトレンド分析で作成される故障リスクのトレンドカーブの一例を示した図。The figure which showed an example of the trend curve of the failure risk created by the failure risk trend analysis of the failure risk prediction part among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、保守・運用シナリオ策定部で実行される保守・運用シナリオを異ならせた場合のリスク予測処理の一例を示した図。The figure which showed an example of the risk prediction process at the time of differing the maintenance / operation scenario performed in the maintenance / operation scenario formulation part among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第１の実施形態による稼働補助システムのうち、製品の主な構成要素を部品と解釈し、故障リスクの予測値の高い順に整理して表示し、グループ分けした例を示した図。The figure which showed the example which interpreted the main component of a product as a part among the operation assistance systems by the 1st Embodiment of this invention, and arranged and displayed in order with the high predicted value of failure risk, and was divided into groups. 本発明の第１の実施形態による稼働補助システムのうち、製品の主な構成要素に属する部品毎に故障リスクの予測値を演算し、その予測値の高い順に部品を整理して表示した図。The figure which calculated the predicted value of failure risk for every part which belongs to the main component of a product among the operation assistance systems by a 1st embodiment of the present invention, and arranged and displayed the part in order with the high predicted value. 本発明の第１の実施形態による稼働補助システムのうち、破壊確率演算から保守・運用シナリオ策定までの手順を示した図。The figure which showed the procedure from destruction probability calculation to maintenance / operation scenario formulation among the operation assistance systems by the 1st Embodiment of this invention. 本発明の第２の実施形態による稼働補助システムの主な構成要素と、稼働補助システムにデータを提供する製品、同型機、類似機およびデータベースと、それらの関係を概略示したブロック図。The block diagram which roughly showed the main component of the operation assistance system by the 2nd Embodiment of this invention, the product which provides data to an operation assistance system, the same model machine, a similar machine, and a database. 本発明の第２の実施形態による稼働補助システムで、製品に含まれる部品の応力履歴から余寿命評価で破壊確率を演算する場合において、等価応力振幅で寿命の確率密度関数を描き、それをベイズ統計に基づき更新する例を示す図。In the operation assistance system according to the second embodiment of the present invention, when the failure probability is calculated by the remaining life evaluation from the stress history of the parts included in the product, the probability density function of the life is drawn with the equivalent stress amplitude, and it is Bayes The figure which shows the example updated based on statistics. 本発明の第２の実施形態による稼働補助システムで、製品に含まれる部品の応力履歴から余寿命評価で破壊確率を演算する場合において、等価応力振幅演算から破断寿命の確率密度関数を更新するまでの手順を示した図。In the operation assistance system according to the second embodiment of the present invention, when the failure probability is calculated by the remaining life evaluation from the stress history of the parts included in the product, the probability density function of the failure life is updated from the equivalent stress amplitude calculation. The figure which showed the procedure of. 本発明の第２の実施形態による稼働補助システムで、製品に含まれる部品の破壊寿命の確率密度関数に基づき破壊確率を演算する場合において、事前に設定した寿命の確率密度関数をベイズ統計に基づき更新する例を示す図。In the operation assistance system according to the second embodiment of the present invention, when the failure probability is calculated based on the probability density function of the failure life of the parts included in the product, the probability density function of the lifetime set in advance is based on Bayesian statistics. The figure which shows the example to update. 本発明の第２の実施形態による稼働補助システムで、時間を含む多変量の確率密度関数で破壊寿命を表し、破壊確率を演算する場合において、事前に設定した確率密度関数をベイズ統計に基づき更新する例を示す図。In the operation assistance system according to the second embodiment of the present invention, when a failure life is expressed by a multivariate probability density function including time and the failure probability is calculated, a preset probability density function is updated based on Bayesian statistics. The figure which shows the example to do. 本発明の第３の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図。The block diagram which roughly showed the main component of the operation assistance system by the 3rd Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and those relationships. 本発明の第３の実施形態による稼働補助システムの保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図。The block diagram which showed roughly the flow of the data exchanged between the main components of the maintenance and operation scenario formulation part of the operation assistance system by the 3rd Embodiment of this invention, and elements. 本発明の第４の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図。The block diagram which roughly showed the main component of the operation assistance system by the 4th Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and those relationship. 本発明の第４の実施形態による稼働補助システムの保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図。The block diagram which showed roughly the flow of the data exchanged between the main components of the maintenance and operation scenario formulation part of the operation assistance system by the 4th Embodiment of this invention, and elements. 本発明の第４の実施形態による稼働補助システムのうち、破壊確率のトレンドカーブを作成する場合の保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図。In the operation assistance system according to the fourth embodiment of the present invention, the main components of the maintenance / operation scenario formulation unit and the flow of data exchanged between the elements when creating a trend curve of the probability of destruction are schematically shown. Block diagram. 本発明の第５の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図。The block diagram which roughly showed the main component of the operation assistance system by the 5th Embodiment of this invention, the product and database which provide the data utilized with an operation assistance system, and those relationships. 本発明の第５の実施形態による稼働補助システムのうち、損傷度のトレンドカーブを作成する場合の保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図。In the operation assistance system according to the fifth embodiment of the present invention, the main components of the maintenance / operation scenario formulation unit and the flow of data exchanged between the elements when creating a trend curve of the degree of damage are schematically shown. Block diagram.
Ａ．概要
 
　本実施形態は上記課題を解決する手段を複数含んでいるが、その一例を挙げるならば、任意の製品の稼働補助システムであって、
前記製品を構成する複数の部品の故障リスクであり、過去から現在に至る前記製品の環境データ、運転データ、設計データ、材料データの少なくともいずれかを含む情報に加え、前記製品の同型機、類似機の故障データの情報に基づいて故障リスクの評価と更新を実行し、現在において前記製品の保守・運用計画を異ならせた場合に変動する複数の前記部品の故障リスク推定値を求める演算装置を備え、演算される複数の前記部品の故障リスク推定値のそれぞれを参照して前記製品の運用や複数の前記部品の保守時期を割り当てる手段を備えることを特徴とすることができる。
　本実施形態によれば、信頼性の高い製品の運用、保守計画を策定し、安定した稼働を行うことができる製品の稼働補助システムを提供することが可能になる。 A. Overview
The present embodiment includes a plurality of means for solving the above-mentioned problems, but if an example is given, it is an operation assisting system for an arbitrary product,
It is the risk of failure of a plurality of parts constituting the product, in addition to information including at least one of environmental data, operation data, design data, material data of the product from the past to the present, the same type machine of the product, similar An arithmetic unit that performs failure risk evaluation and update based on machine failure data information and obtains failure risk estimates for a plurality of the parts that fluctuate when the maintenance / operation plan of the product is changed at present. And means for assigning operation of the product and maintenance time for the plurality of parts by referring to each of the estimated failure risk values of the plurality of parts.
According to the present embodiment, it is possible to provide a product operation assistance system capable of formulating a highly reliable product operation and maintenance plan and performing stable operation.
Ｂ．稼働補助装置及び風力発電システム
 
　以下、図面を用いて本発明の実施形態を説明する。
 
［第１の実施の形態］
 
　以下、図１～１０を用いて、本発明の第１の実施形態による稼働補助システムについて説明する。本実施形態では、製品１として風力発電プラントを挙げているが、本発明及び/又は本実施形態の適用は風力発電プラントに限定されるものではない。
　第１の実施形態によれば、製品を構成する複数部品をグループ化することによって、製品を構成する複数部品の高精度な信頼性の評価値を考慮して、製品の運用、保守計画を実施可能とすることができる。 B. Operation assistance device and wind power generation system
Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[First Embodiment]

Hereinafter, the operation assistance system according to the first embodiment of the present invention will be described with reference to FIGS. In the present embodiment, a wind power plant is cited as the product 1, but the application of the present invention and / or the present embodiment is not limited to the wind power plant.
According to the first embodiment, a product operation / maintenance plan is implemented by grouping multiple parts that make up a product, taking into account highly accurate reliability evaluation values of the multiple parts that make up the product Can be possible.
　図１は、本実施形態の製品の稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図である。図１に示される稼働補助システム１００は、故障リスク評価部２、保守・運用シナリオ策定部３を備える。保守・運用シナリオ策定部３には、製品に含まれる複数の部品を対象とし、保守・運用計画を異ならせた場合に変動する該部品の故障リスクを予測する故障リスク予測部４を含んでいる。図では省略されているが、稼働補助システム１００は、入力部、表示部、他の装置への出力部を備える。
　製品１には使用環境や運転状態を計測するためのセンサが多数実装されている。それらのセンサで計測された環境データ、運転データは、稼働補助システム１００の故障リスク評価部２に送られ、評価に利用される。ここで環境データとは、製品が曝される環境に関するデータを含むデータであり、風力発電プラントの場合、例えば風車の風速、風向などの風況データは環境データに含まれる。洋上に設置された風力発電プラントの場合、風況データに加えて、波長や波高などの海況データも環境データの範疇である。また運転データとは、速度、加速度、回転速度、回転角など、製品の稼働状態に関連するデータである。風力発電プラントの場合、風車の発電量、発電機の回転速度、アジマス角、ナセル角などは運転データの範疇である。プラントでは、環境データ、運転データは制御計測（ＳＣＡＤＡ）として計測されることが多い。ただし、状態監視（ＣＭＳ）や構造物監視（ＳＨＭ）を含むプラントに関しては、これらで計測されるデータでも製品の使用環境や稼働状態に関連するデータであれば、本実施形態の環境データや運転データに含まれる。
環境データや運転データに加えて、設計データや材料データが故障リスク評価部２で利用される。ここで設計データは、例えば製品の図面など製品形状に関するデータを含んでいる。また、材料データは、製品を構成する材料の特性や、ボルト締結や溶接継手などの構造の特性を含んでいる。 FIG. 1 is a block diagram schematically illustrating main components of a product operation support system according to the present embodiment, a product and database that provide data used in the operation support system, and a relationship between them. An operation assistance system 100 shown in FIG. 1 includes a failure risk evaluation unit 2 and a maintenance / operation scenario formulation unit 3. The maintenance / operation scenario formulation unit 3 includes a failure risk prediction unit 4 that targets a plurality of parts included in a product and predicts a failure risk of the parts that changes when a maintenance / operation plan is changed. . Although not shown in the figure, the operation assistance system 100 includes an input unit, a display unit, and an output unit to another device.
The product 1 is equipped with a number of sensors for measuring the use environment and the operating state. Environmental data and operation data measured by these sensors are sent to the failure risk evaluation unit 2 of the operation assisting system 100 and used for evaluation. Here, the environmental data is data including data related to the environment to which the product is exposed. In the case of a wind power plant, for example, wind condition data such as wind speed and direction of a windmill is included in the environmental data. In the case of a wind power plant installed on the ocean, in addition to wind condition data, sea condition data such as wavelength and wave height is also a category of environmental data. The operation data is data related to the operating state of the product, such as speed, acceleration, rotation speed, and rotation angle. In the case of a wind power plant, the amount of power generated by a windmill, the rotational speed of a generator, the azimuth angle, the nacelle angle, and the like are categories of operation data. In a plant, environmental data and operation data are often measured as control measurement (SCADA). However, regarding a plant including state monitoring (CMS) and structure monitoring (SHM), even if the data measured by these is data related to the use environment or operating state of the product, the environmental data and operation of this embodiment Included in the data.
In addition to environmental data and operation data, design data and material data are used in the failure risk evaluation unit 2. Here, the design data includes data relating to the product shape such as a product drawing. The material data includes the characteristics of the materials constituting the product and the characteristics of structures such as bolt fastening and welded joints.
　図２は、故障リスク評価部２で実行される処理を概略示したブロック図である。
　故障リスク評価部２は、環境データ、運転データ、設計データおよび材料データの少なくともいずれかを利用して、故障リスクを演算する。時刻ｔ１において、製品に含まれるある部品ｐの故障リスクＲＳ（ｔ１，ｐ）は、対象部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）と、対象部品ｐが壊れた場合の影響度Ｃ（ｐ）から、次式で計算できる。なお、部品ｐ毎の影響度Ｃ（ｐ）のデータは、影響度データベース８に予め蓄積されている。
 
　ＲＳ（ｔ１，ｐ）＝Ｃ（ｐ）×Ｆ（ｔ１）　・・・（１）
 
式（１）の故障リスクＲＳ（ｔ１，ｐ）を信頼性の指標とすることで、ある部品が故障した場合に及ぶ影響の大きさを考慮して、部品の保守・運用を計画することができる。 FIG. 2 is a block diagram schematically showing processing executed by the failure risk evaluation unit 2.
The failure risk evaluation unit 2 calculates a failure risk using at least one of environmental data, operation data, design data, and material data. At time t1, the failure risk RS (t1, p) of a certain part p included in the product is the destruction probability F (t1) of the target part p at the time t1 and the degree of influence C ( From p), it can be calculated by the following equation. Note that the data of the degree of influence C (p) for each part p is stored in the influence degree database 8 in advance.

RS (t1, p) = C (p) × F (t1) (1)

By using the failure risk RS (t1, p) of the equation (1) as an index of reliability, it is possible to plan the maintenance and operation of a component in consideration of the magnitude of the effect when a certain component fails. it can.
　破壊確率Ｆ（ｔ１）は、余寿命評価や予兆診断で計算することができる。
　まず、余寿命評価について説明する。
　製品に繰り返し負荷が作用して発生する疲労現象を対象とし、余寿命評価で破壊確率Ｆ（ｔ１）を求めるには、まず、環境データ、運転データおよび設計データを利用して、過去から現時点ｔ１までに部品に発生した応力履歴を計算する。次に、この応力履歴に対して、レインフロー法などの頻度解析法を適用し、ある大きさの応力がどのような頻度で発生しているかを整理した応力頻度分布を作成する。そして、応力頻度分布と対象部品に関連する材料データを利用して、破壊確率Ｆ（ｔ１）を求める。 The failure probability F (t1) can be calculated by remaining life evaluation or predictive diagnosis.
First, the remaining life evaluation will be described.
In order to obtain the failure probability F (t1) in the remaining life evaluation for the fatigue phenomenon caused by the repeated load acting on the product, first, from the past to the current t1 using the environmental data, operation data and design data. Calculate the history of stress that has occurred in the parts until now. Next, a frequency analysis method such as a rain flow method is applied to the stress history to create a stress frequency distribution that organizes how often a certain amount of stress is generated. Then, the fracture probability F (t1) is obtained using the stress frequency distribution and the material data related to the target part.
　図３は、対象製品に含まれる部品の応力履歴から余寿命評価で破壊確率を演算するのに必要なＰ－Ｓ－Ｎ線図である。
　ここで利用される材料データは、Ｐ－Ｓ－Ｎ線図と呼ばれる図３の疲労寿命曲線であることが望ましく、本実施例では設計・材料データベース５に蓄積されている。Ｐ－Ｓ－Ｎ線図は、縦軸の各応力振幅で疲労試験を実施して得られる疲労寿命の確率密度関数２０から破壊確率Ｐ％となる繰り返し数を求め、それらを連結して図示したものである（図３）。 FIG. 3 is a PSN diagram necessary for calculating the failure probability by the remaining life evaluation from the stress history of the parts included in the target product.
The material data used here is preferably the fatigue life curve of FIG. 3 called a PSN diagram, and is stored in the design / material database 5 in this embodiment. In the PSN diagram, the number of repetitions having a fracture probability P% is obtained from the probability density function 20 of fatigue life obtained by carrying out a fatigue test at each stress amplitude on the vertical axis, and these are connected and illustrated. (FIG. 3).
　図４は、対象製品に含まれる部品に生じる応力頻度分布とＰ－Ｓ－Ｎ線図から損傷度を演算する方法を示した図である。
　図３のように、各応力振幅での疲労寿命の確率密度関数２０が全て同一と仮定できる場合、次の手順で破壊確率Ｆ（ｔ１）が求められる。まず、図４に示すように、過去から現時点ｔ１までに発生した応力履歴に頻度解析法を適用して得られる応力頻度分布２１と、予め定められた破壊確率Ｐ％の疲労寿命曲線２２から、疲労破壊Ｐ％に対する損傷度Ｄ（ｔ１）が次式で計算できる。
 
　Ｄ（ｔ１）＝（ｎ１／Ｎ１）＋（ｎ２／Ｎ２）＋…＋（ｎｍ／Ｎｍ）　・・・（２）
 
ここで、ｎ１、ｎ２、ｎｍはそれぞれ、応力履歴の頻度解析によって得られる応力振幅Ｓ１、Ｓ２，Ｓｍの繰り返し数である（ｍは整数）。また、Ｎ１，Ｎ２，Ｎｍはそれぞれ、応力振幅Ｓ１、Ｓ２，Ｓｍを繰り返して負荷した場合に、疲労破壊が破壊確率Ｐ％で発生する破断繰り返し数である。疲労寿命の確率密度関数２０が応力振幅に依存せず、全て同一と仮定した場合、損傷度Ｄ（ｔ１）を発生させる繰り返し数Ｎ（ｔ１）は次式で求められる。
 
　Ｎ（ｔ１）＝Ｄ（ｔ１）×Ｎｐ　・・・（３）
 
Ｎｐは予め定められた破壊確率Ｐ％の繰り返し数であり、入力部(図示せず。)により応力振幅を適当なＳｉに定めることで（例えば、平均値、中間値、それらに近い値等）図４中に示すように、Ｎ（ｔ１）と疲労寿命の確率密度関数２０から、図４の式により、破壊確率Ｆ（ｔ１）を求めることができる。すなわち、製品の運転開始から現在までの経過時間をｔ１とすると、破壊確率Ｆ（ｔ１）は密度関数ｆ（Ｎ）を０からＮ（ｔ１）まで積分して得られる値である。 FIG. 4 is a diagram showing a method of calculating the damage degree from the stress frequency distribution generated in the parts included in the target product and the PSN diagram.
As shown in FIG. 3, when it can be assumed that the probability density functions 20 of fatigue life at each stress amplitude are all the same, the fracture probability F (t1) is obtained by the following procedure. First, as shown in FIG. 4, from the stress frequency distribution 21 obtained by applying the frequency analysis method to the stress history generated from the past to the present time t1, and the fatigue life curve 22 with a predetermined failure probability P%, The degree of damage D (t1) with respect to fatigue failure P% can be calculated by the following equation.

D (t1) = (n 1 / N 1 ) + (n 2 / N 2 ) +... + (N m / N m ) (2)

Here, n 1 , n 2 , and n m are respectively the number of repetitions of stress amplitudes S 1 , S 2 , and S m obtained by frequency analysis of stress history (m is an integer). N 1 , N 2 , and N m are the number of repetitions of fracture at which fatigue failure occurs with a failure probability P% when stress amplitudes S 1 , S 2 , and S m are repeatedly applied. When the fatigue life probability density function 20 does not depend on the stress amplitude and is assumed to be all the same, the number of repetitions N (t1) for generating the damage degree D (t1) can be obtained by the following equation.

N (t1) = D (t1) × Np (3)

Np is the advance number of repetitions of the failure probability P% defined, an input unit (not shown.) By by determining the stress amplitude to the appropriate S i (e.g., average value, intermediate value, close to their values, etc. As shown in FIG. 4, the fracture probability F (t1) can be obtained from N (t1) and the probability density function 20 of fatigue life by the equation of FIG. That is, if the elapsed time from the start of operation of the product to the present is t1, the fracture probability F (t1) is a value obtained by integrating the density function f (N) from 0 to N (t1).
　つぎに、余剰寿命評価についての他の例を説明する。
　図５は、破壊寿命の確率密度関数を利用して破壊確率を演算する他の方法を示した図である。
　また、破壊までの破壊確率Ｆ（ｔ１）は、図５のような寿命の確率密度関数ｆ（ｔ１）を直接定義して求めることもできる。すなわち、製品の運転開始から現在までの経過時間をｔ１とすると、破壊確率Ｆ（ｔ１）は密度関数ｆ（ｔ）を０からｔ１まで積分して得られる値である。ここで、破壊確率５０％となる時間を寿命Ｔと定義すれば、現時点ｔ１と寿命時間Ｔの差を余寿命と考えることができるため、このように寿命の確率密度関数を直接定義する方法も余寿命評価と解釈できる。なお、寿命の確率密度関数ｆ（ｔ１）のデータは、例えば、対象製品毎に故障リスク評価部２の内部メモリ等の適宜のメモリを予め記憶しておくことができる。また、寿命の確率密度関数ｆ（ｔ１）のデータは、環境データ、運転データ等を使用して、予め定めることができる。 Next, another example of excess life evaluation will be described.
FIG. 5 is a diagram showing another method for calculating the fracture probability using the probability density function of the fracture life.
Further, the failure probability F (t1) until the failure can be obtained by directly defining the probability density function f (t1) of the lifetime as shown in FIG. That is, assuming that the elapsed time from the start of operation of the product to the present time is t1, the fracture probability F (t1) is a value obtained by integrating the density function f (t) from 0 to t1. Here, if the time at which the fracture probability is 50% is defined as the life T, the difference between the current time t1 and the life time T can be considered as the remaining life, so there is a method for directly defining the probability density function of the life in this way. This can be interpreted as a remaining life evaluation. The life probability density function f (t1) data can be stored in advance in an appropriate memory such as the internal memory of the failure risk evaluation unit 2 for each target product. Further, the data of the probability density function f (t1) of the lifetime can be determined in advance using environmental data, operation data, and the like.
　つぎに、予兆診断について説明する。
　図６は、製品の計測データから破壊に関連する物理量を選択し、それを適切に変換して寿命の確率密度関数を求める方法を示した図である。
　一方の予兆診断では、予め対象部品の正常時と異常時の運転データを整理しておき、現時点の運転データを監視して対象部品の故障を判定する。異常時の運転データ群を図６のように破壊の確率密度関数として捉え、現時点の運転データの位置を図６にプロットすることで、現時点ｔ１での破壊確率Ｆ（ｔ１）を求めることができる。異常時の運転データから着目する部品の破壊に関連する物理量を予め選択し、選択した物理量を確率変数とする確率密度関数を作成する場合や、図６のように、着目する部品の破壊と関連する形式に運転データに含まれる物理量を変換し、その変換物理量ｘ１’、ｘ２’を確率変数として確率密度関数を作成する場合もある。ここで、変換物理量とは例えば、運転データに含まれる加速度データを高速フーリエ変換して求められる加速度スペクトルのうち、ある特定の周波数のスペクトル値が挙げられる。なお、確率密度関数は、例えば、対象製品毎に故障リスク評価部２の内部メモリ又は設計・材料データベース５等の適宜のメモリを予め記憶しておくことができる。また、確率密度関数は、環境データ、運転データ等を使用して、予め定めることができる。 Next, predictive diagnosis will be described.
FIG. 6 is a diagram illustrating a method of selecting a physical quantity related to destruction from measurement data of a product and appropriately converting it to obtain a probability density function of a lifetime.
On the other hand, in the predictive diagnosis, the operation data when the target part is normal and abnormal are arranged in advance, and the current operation data is monitored to determine the failure of the target part. A failure probability F (t1) at the current time t1 can be obtained by capturing the abnormal operation data group as a probability density function of the failure as shown in FIG. 6 and plotting the position of the current operation data in FIG. . When a physical quantity related to the destruction of the target part is selected in advance from the operation data at the time of abnormality and a probability density function using the selected physical quantity as a random variable is created, or related to the destruction of the target part as shown in FIG. In some cases, a physical quantity included in the operation data is converted into a format to be generated, and a probability density function is created using the converted physical quantities x1 ′ and x2 ′ as random variables. Here, the converted physical quantity includes, for example, a spectrum value of a specific frequency among acceleration spectra obtained by performing fast Fourier transform on acceleration data included in driving data. For example, the probability density function may store in advance an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product. The probability density function can be determined in advance using environmental data, operation data, or the like.
　つぎに、Ｃ（ｐ）について説明する。
　影響度データベース８には式（１）に含まれる部品ｐ毎の影響度Ｃ（ｐ）のデータが蓄積されている。影響度Ｃ（ｐ）として、実際にある部品ｐが壊れた場合に要する全て又は予め定められた範囲等の費用を採用すれば、故障リスクＲＳ（ｔ１，ｐ）は、現時点ｔ１において故障によって生じる全て又は予め定められた範囲等の損失費用の期待値と考えることができる。ある部品が壊れた場合に要する費用には、新たな部品そのものの費用、部品の交換費用、部品の運搬費用、製品の稼働停止による発電機会の損失費用などが含まれる。一方、具体的な費用でなく、部品が壊れた場合に生じる影響の大きさに応じて整数を割り当て、その整数を影響度Ｃ（ｐ）として採用することもできる。このような場合、故障リスクＲＳ（ｔ１，ｐ）を部品間で相対比較し、どの部品の信頼性に留意すべきかを判断することができる。 Next, C (p) will be described.
The influence degree database 8 stores data of the influence degree C (p) for each part p included in the equation (1). If costs such as all or a predetermined range required when an actual part p is broken are used as the degree of influence C (p), the failure risk RS (t1, p) is caused by a failure at the current time t1. It can be considered as an expected value of loss cost such as all or a predetermined range. The cost required when a part is broken includes the cost of the new part itself, the replacement cost of the part, the transportation cost of the part, and the loss cost of the power generation opportunity due to the shutdown of the product. On the other hand, it is possible to assign an integer according to the magnitude of the influence that occurs when the part is broken, not the specific cost, and adopt the integer as the influence degree C (p). In such a case, the failure risk RS (t1, p) can be relatively compared between the components, and it can be determined which component should be considered for reliability.
　こうして演算した故障リスクＲＳ（ｔ１，ｐ）は、保守・運用データ、環境データ、運転データ、設計データ、材料データとともに、保守・運用シナリオ策定部３に入力される。 The failure risk RS (t1, p) calculated in this way is input to the maintenance / operation scenario formulation unit 3 together with the maintenance / operation data, environmental data, operation data, design data, and material data.
　図７は、保守・運用シナリオ策定部で実行される処理を概略示したブロック図である。保守・運用シナリオ策定部３は、故障リスク予測部４を有し、故障リスク予測部４では、故障リスク評価部２から送られてきた時刻ｔ１での故障リスクＲＳ（ｔ１、ｐ）と、故障リスク評価部２から既に送られてきて故障リスクデータベース３０に予め記憶された過去から時刻ｔ１までの各時刻ｔｎ（ｎ＝０、－１、－２、－３、・・・）の複数の故障リスクＲＳ（ｔｎ、ｐ）と、環境データ、運転データから故障リスクの変動トレンドを分析する。 FIG. 7 is a block diagram schematically showing processing executed by the maintenance / operation scenario formulation unit. The maintenance / operation scenario formulation unit 3 includes a failure risk prediction unit 4. The failure risk prediction unit 4 includes a failure risk RS (t 1, p) at time t 1 sent from the failure risk evaluation unit 2 and a failure. A plurality of failures at each time tn (n = 0, −1, −2, −3,...) From the past to time t1 that have already been sent from the risk evaluation unit 2 and stored in advance in the failure risk database 30 The fluctuation trend of the failure risk is analyzed from the risk RS (tn, p), the environmental data, and the operation data.
　図８は、故障リスク予測部の故障リスクトレンド分析で作成される故障リスクのトレンドカーブの一例を示した図である。
　例えば、日本にある風力発電プラントの場合、冬季に風が強い傾向にあるため、部品によっては図８のように横軸を時間、縦軸を故障リスクとしたトレンドカーブを描くことができる。ただし、季節変動があったとしても、破壊確率の演算に使用するＰ－Ｓ－Ｎ線図や確率密度関数の分布形状によっては図８のように規則性のあるトレンドカーブが得られない場合も少なくない。さらに、同じ風力発電プラントを構成する部品であっても、季節変動と同等あるいは季節変動以上に、短期的な風の乱れ度合いによる故障リスクＲＳ（ｔ１、ｐ）の変動が大きい場合もあるため、部品毎にトレンド変動に関与する物理量を環境データや運転データから選択し、時間に加えて選択した物理量を変数として故障リスクのトレンドカーブを描く必要がある。すなわち、故障リスク予測部４は、時間をｔ、選択した物理量をｘとすると、製品や製品を構成する部品に応じて、次式（４）、（５）などを選択して故障リスクのトレンドカーブＲＳを決定する。
　
　ＲＳ（ｔ，ｐ）＝ｇ１（ｔ，ｐ）　・・・（４）
　ＲＳ（ｔ，ｘ，ｐ）＝ｇ２（ｔ，ｘ，ｐ）　・・・（５）
　
式（４）（５）は、時刻ｔや物理量ｘが変数の故障リスクのトレンドカーブであるが、ある時点まで遡った過去から現在までの物理量や誤差項を含む自己回帰移動平均モデルや、時刻、環境データ、運転データを入力として学習したニューラルネットワークをトレンドカーブとして採用することもできる。
　なお、簡単な数式としては、例えば、
　
　ＲＳ（ｔ，ｘ，ｐ）＝α（ｐ）・ｔ＋β（ｐ）・ｘ＋γ（ｐ）
　（α，β，γは定数，ただし部品に依存）
　
等があるが、これに限らない。 FIG. 8 is a diagram showing an example of a failure risk trend curve created by failure risk trend analysis of the failure risk prediction unit.
For example, in the case of a wind power plant in Japan, wind tends to be strong in winter, so that depending on the part, a trend curve can be drawn with time on the horizontal axis and failure risk on the vertical axis as shown in FIG. However, even if there is a seasonal variation, a regular trend curve as shown in FIG. 8 may not be obtained depending on the PSN diagram and probability density function distribution shape used for the calculation of failure probability. Not a few. Furthermore, even if the components make up the same wind power plant, the fluctuation of the failure risk RS (t1, p) due to the short-term wind turbulence may be greater than or equal to the seasonal fluctuation, It is necessary to select a physical quantity related to trend fluctuation for each part from environmental data and operation data, and draw a trend curve of failure risk using the selected physical quantity as a variable in addition to time. That is, the failure risk prediction unit 4 selects the following equations (4), (5), etc. according to the product and the parts constituting the product, where t is the time and x is the selected physical quantity. The curve RS is determined.

RS (t, p) = g1 (t, p) (4)
RS (t, x, p) = g2 (t, x, p) (5)

Equations (4) and (5) are the trend curves of failure risk when the time t and the physical quantity x are variables, but the autoregressive moving average model including the physical quantity and error term from the past to the present time, It is also possible to adopt a neural network learned by inputting environmental data and driving data as a trend curve.
As a simple formula, for example,

RS (t, x, p) = α (p) · t + β (p) · x + γ (p)
(Α, β, and γ are constants, but depend on parts)

However, it is not limited to this.
　故障リスク予測部４は、故障リスクのトレンドカーブ、保守・運用データ、保守・運用シナリオ策定１２で定められた保守・運用シナリオに基づき、故障リスク予測１１を実施する。保守・運用データとは、過去のデータであり、例えば製品の定期点検の情報、運用を異ならせるための制御変更情報、故障による点検実施の情報を含んでいる。保守・運用シナリオとは、例えば、何年何月にどの部品を交換する、運用をどのようにする等といった計画を指す。保守・運用シナリオ策定１２は、予め定められた手法により自動で保守・運用シナリオを作成してもよいし、入力部からマニュアルで保守・運用シナリオを入力してもよい。 The failure risk prediction unit 4 executes the failure risk prediction 11 based on the failure risk trend curve, maintenance / operation data, and the maintenance / operation scenario defined in the maintenance / operation scenario formulation 12. The maintenance / operation data is past data, and includes, for example, information on periodic inspection of products, control change information for making operations different, and information on inspection execution due to failure. The maintenance / operation scenario refers to, for example, a plan such as how to replace which part in what year and month, how to operate, and the like. The maintenance / operation scenario formulation 12 may automatically create a maintenance / operation scenario by a predetermined method, or may manually input a maintenance / operation scenario from an input unit.
　図９は、保守・運用シナリオ策定部で実行される保守・運用シナリオを異ならせた場合のリスク予測処理の一例を示した図である。
　将来においても現在採用している定期点検、運用方法（製品の制御方法など）を継続する場合、現時点から予め定められた時間ΔＴ進んだ将来の故障リスク（予測リスク）ａは、図９中の予測リスクaのように、これまでのトレンドカーブに沿ったものとなる。 FIG. 9 is a diagram illustrating an example of risk prediction processing when the maintenance / operation scenario executed by the maintenance / operation scenario formulation unit is different.
When the periodic inspection and operation method (product control method, etc.) currently employed in the future are continued, the future failure risk (predicted risk) a advanced by a predetermined time ΔT from the present time is shown in FIG. Like forecast risk a, it will be along the trend curve so far.
　次に、図７で示すように、保守・運用シナリオ策定部３は、予測リスクを使って保守・運用シナリオ策定１２を行う。すなわち、予測リスクの値に基づき、保守運用者等による入力部からの入力によりマニュアルで又は予め定められた処理により自動で、製品の保守や運用方法の変更が検討される。例えば、予測リスクが予め定められた閾値より高い場合、より穏やかな製品の運用方法への変更が保守・運用シナリオ策定１２で提案され、予測リスクが予め定められた閾値より低い場合は、より激しい製品の運用方法や頻繁な保守点検が提案される。提案された保守・運用シナリオは故障リスク予測１１に再び送られ、その保守・運用シナリオに応じて、図９中のように、将来リスクｂ又はｃが予測される。保守や運用を異ならせた場合の将来リスクｂ又はｃの予測のために、時刻ｔ、故障リスクに影響を及ぼす物理量ｘ（環境データ、運転データから選択）に加え、保守・運転データから故障リスクに影響を及ぼす物理量ｙを選択し、時刻ｔ、物理量ｘとｙを変数とする次式の故障リスク予測モデルｇ３あるいはｇ４を利用することができる。
 
　ＲＳ（ｔ＋ΔＴ，ｙ，ｐ）＝ｇ３（ｔ＋ΔＴ，ｙ，ｐ）　・・・（６）
　ＲＳ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）＝ｇ４（ｔ＋ΔＴ，ｘ，ｙ，ｐ）
　　　　　　　　　　　　　　　　　　　　　　　　　　　　・・・（７）
 
式（６）（７）は、時刻ｔ＋ΔＴや将来の物理量ｘ、ｙが変数のトレンドカーブであるが、ある時点まで遡った過去から時刻ｔ＋ΔＴまでの物理量や誤差項を含む自己回帰移動平均モデルや、時刻、環境データ、運転データ、保守・運用データを入力として学習したニューラルネットワークを予測モデルとして採用することもできる。 Next, as shown in FIG. 7, the maintenance / operation scenario formulation unit 3 performs the maintenance / operation scenario formulation 12 using the predicted risk. That is, based on the predicted risk value, the maintenance of the product and the change of the operation method are examined manually by the input from the input unit by the maintenance operator or automatically by a predetermined process. For example, when the predicted risk is higher than a predetermined threshold, a more gentle change to the product operation method is proposed in the maintenance / operation scenario formulation 12, and when the predicted risk is lower than the predetermined threshold, it is more severe Product operation methods and frequent maintenance inspections are proposed. The proposed maintenance / operation scenario is sent again to the failure risk prediction 11, and the future risk b or c is predicted as shown in FIG. 9 according to the maintenance / operation scenario. In order to predict future risk b or c when maintenance or operation is different, failure risk from maintenance / operation data in addition to physical quantity x (selected from environmental data and operation data) that affects failure risk at time t The physical quantity y that affects the physical quantity y is selected, and the failure risk prediction model g3 or g4 of the following equation using the physical quantities x and y as variables can be used.

RS (t + ΔT, y, p) = g3 (t + ΔT, y, p) (6)
RS (t + ΔT, x, y, p) = g4 (t + ΔT, x, y, p)
... (7)

Equations (6) and (7) are trend curves in which the time t + ΔT and the future physical quantities x and y are variables, and the autoregressive moving average model including the physical quantities and error terms from the past going back to a certain time to the time t + ΔT A neural network learned by inputting time, environmental data, operation data, and maintenance / operation data can also be used as a prediction model.
　つぎに、図１０は、製品の主な構成要素を部品と解釈し、故障リスクの予測値の高い順に整理して表示し、グループ分けした例を示した図である。グループ化を考慮して図７の保守・運用シナリオ策定１２が行われる。例えば、どのグループは何年後に保守する等の計画が策定される。
　図９の故障リスク予測は、製品を構成する全ての部品を対象として実施することが可能である。ただし、故障リスク（又は、予測リスク又は将来リスク）が高い部品のみを対象として故障リスクを予測することで、予測に必要な工数を削減でき、効率的に保守や運用を計画することができる。保守・運用シナリオ策定部３は、故障リスクの予測値については、例えば図１０のように、予測値が最大となる部品、その最大値と併せて、故障リスク（又は、予測リスク又は将来リスク）の予測値の高いものから順番に整理して、表示部に表示する。グループ化の具体的処理については、例えば、全ての部品の故障リスクがある閾値を超えないように、グループ毎の上限及び下限の閾値により、グループと保守時期を割り当てることができる。例えば、遺伝的アルゴリズムによって、このような割り当てが可能である。保守・運用シナリオ策定部３の保守・運用シナリオ策定１２では、図１０を参照し、保守運用者等による入力部からの入力によりマニュアルで又は予め定められた処理により自動で、グループ毎に、どの部品を、いつ保守するか等の保守運用内容を計画する。あるいは、入力部からマニュアルで保守運用内容等の保守・運用シナリオを入力してもよい。保守・運用シナリオ策定部３の保守・運用シナリオ策定１２では、計画された保守運用内容を適宜の記憶部に記憶及び／又は表示部に表示する。故障リスクの予測値（予測リスク又は将来リスク等）が大きさに応じて整理してあれば、例えば図１０に示すように、予め定められた複数の閾値により、グループＡ、グループＢ、グループＣなどに部品群をグループ化して保守を計画することができる。このようなグループ化による保守計画により、例えば、グループＡに属する部品の交換を最も直近に計画されていた保守時期に実施するなど、予め計画された保守スケジュールに合わせて、故障リスクの予測値（予測リスク又は将来リスク）の高い部品の交換などを行うことができる。このような保守時期を割り当てるためのグループ化は、遺伝的アルゴリズムや分岐限定法などの組み合わせ最適化法を利用して実施することができる。 Next, FIG. 10 is a diagram illustrating an example in which main components of the product are interpreted as parts, arranged and displayed in descending order of predicted value of failure risk, and grouped. The maintenance / operation scenario formulation 12 shown in FIG. 7 is performed in consideration of grouping. For example, a plan may be established such as which group will be maintained after how many years.
The failure risk prediction in FIG. 9 can be performed on all parts constituting the product. However, by predicting failure risk only for parts with high failure risk (or prediction risk or future risk), the man-hours required for prediction can be reduced, and maintenance and operation can be efficiently planned. For example, as shown in FIG. 10, the maintenance / operation scenario formulation unit 3 has a failure risk (or a prediction risk or a future risk) together with a component having the maximum prediction value and the maximum value as shown in FIG. Are arranged in descending order of the predicted values and displayed on the display unit. As for the specific processing of grouping, for example, the group and the maintenance time can be assigned according to the upper and lower thresholds for each group so that the failure risk of all parts does not exceed a certain threshold. For example, such an assignment is possible by a genetic algorithm. The maintenance / operation scenario formulation unit 12 of the maintenance / operation scenario formulation unit 3 refers to FIG. 10, which is manually input by the maintenance operator or the like from the input unit or automatically by a predetermined process for each group. Plan maintenance operations such as when to maintain parts. Alternatively, a maintenance / operation scenario such as maintenance operation contents may be manually input from the input unit. In the maintenance / operation scenario formulation unit 12 of the maintenance / operation scenario formulation unit 3, the planned maintenance operation content is stored in an appropriate storage unit and / or displayed on the display unit. If the predicted value of the failure risk (predicted risk or future risk, etc.) is arranged according to the magnitude, for example, as shown in FIG. 10, group A, group B, group C are determined according to a plurality of predetermined thresholds. For example, maintenance can be planned by grouping parts into groups. According to such a maintenance plan based on grouping, for example, replacement of parts belonging to group A is performed at the maintenance time that was most recently planned. It is possible to replace parts with high prediction risk or future risk). Such grouping for assigning maintenance times can be performed using a combinational optimization method such as a genetic algorithm or a branch and bound method.
　また、図１１は、製品の主な構成要素に属する部品毎に故障リスクの予測値を演算し、その予測値の高い順に部品を整理して表示した図である。
　上述した図１０は、製品に含まれるコンポーネントを部品と解釈し、部品と故障リスクの予測値の関係を整理したものである。例えば、どのコンポーネントにどの部品が含まれるかを定めたファイルを適宜の記憶部に予め記憶しておき、保守・運用シナリオ策定部３は、それを参照してコンポーネント、部品、故障リスクを表示部に表示することができる。このようにコンポーネントを対象として故障リスクの予測値（予測リスク又は将来リスク）を整理することで、製品に影響を及ぼすコンポーネントを明確化することができる。一方、図１１のように、コンポーネントに含まれる部品単位で故障リスクの予測値を整理することもできる。このように部品単位で故障リスクの予測値を整理することで、保守に向けて準備すべき部品を明らかにすることができる。このような効果を得るために、本実施形態の稼働補助システムは図１０、１１のように故障リスクの予測値を整理して表示する装置を備えている。 FIG. 11 is a diagram in which predicted values of failure risk are calculated for each part belonging to main components of the product, and the parts are arranged and displayed in descending order of the predicted value.
FIG. 10 described above interprets the components included in the product as parts, and arranges the relationship between the parts and the predicted value of the failure risk. For example, a file that defines which component is included in which component is stored in advance in an appropriate storage unit, and the maintenance / operation scenario formulation unit 3 displays the component, component, and failure risk by referring to the file. Can be displayed. Thus, by arranging the predicted value (predicted risk or future risk) of the failure risk for the component, it is possible to clarify the component that affects the product. On the other hand, as shown in FIG. 11, the predicted value of the failure risk can be arranged for each part included in the component. Thus, by organizing the predicted values of failure risk in units of parts, it is possible to clarify the parts to be prepared for maintenance. In order to acquire such an effect, the operation assistance system of this embodiment is provided with the apparatus which arranges and displays the predicted value of a failure risk like FIG.
　稼働補助システム１００の故障リスク評価部２での故障リスク評価、保守・運用シナリオ策定部３での故障リスク予測、保守・運用シナリオ策定はある時間間隔で実行される。この時間間隔は環境データや運転データが計測される時間間隔と同一でも良いし、異なっていても良い。風力発電プラントに採用されている制御計測（ＳＣＡＤＡ）では、環境データや運転データの統計値（最大値、最小値、平均値など）が、例えば１０分間隔で演算され、その統計値はＰＣなどで構成したサーバに蓄積される。例えば、この１０分間隔に合わせて故障リスク評価、故障リスク予測を実行することとし、故障リスク予測のΔＴを例えば数カ月とすれば、本実施形態の稼働補助システムで、風力発電プラントに含まれる部品の故障を十分に予測しながら風車を安定して稼働させることができる。 The failure risk evaluation in the failure risk evaluation unit 2 of the operation assistance system 100, the failure risk prediction in the maintenance / operation scenario formulation unit 3, and the maintenance / operation scenario formulation are executed at certain time intervals. This time interval may be the same as or different from the time interval at which the environmental data and operation data are measured. In control measurement (SCADA) adopted in wind power plants, environmental data and operational data statistical values (maximum value, minimum value, average value, etc.) are calculated at 10-minute intervals, for example, and the statistical values are PCs or the like. Is stored in the server configured with. For example, if failure risk evaluation and failure risk prediction are executed at intervals of 10 minutes, and ΔT of failure risk prediction is set to several months, for example, the operation assistance system according to the present embodiment can include components included in the wind power plant. The wind turbine can be operated stably while sufficiently predicting the failure of the wind turbine.
　図１２に、第１の実施形態による稼働補助システムのフローチャートを示す。まず、故障リスク評価部２は、対象とする部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）と故障リスクＲＳ（ｔ１，ｐ）を演算する（Ｓ１１、Ｓ１２）。次に、故障リスク予測部４は、故障リスクのトレンドカーブＲＳ（ｔ，ｘ，ｐ）と将来の故障リスクＲＳ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）を演算する（Ｓ１３、Ｓ１４）。そして、計算された将来の故障リスクに基づき、予め定められた閾値と比較する等により自動で、又は、保守・運用者等のマニュアルで入力部からの入力により、保守・運用を変更するかどうかを判断する。そして、変更の必要ありという判断になれば、予め定められた処理により自動で、又は、保守・運用者等による入力部からの入力によりマニュアルで、保守・運用シナリオ策定１２に変更する物理量・条件のパラメータ等が設定・入力され、その設定・入力に従い保守・運用シナリオ策定部３は、保守・運用シナリオを策定する（Ｓ１５、Ｓ１６、Ｓ１４）。このような手順を、前述したように、例えば１０分間隔で繰り返し、製品の安定稼働を補助する。 FIG. 12 shows a flowchart of the operation assistance system according to the first embodiment. First, the failure risk evaluation unit 2 calculates the failure probability F (t1) and the failure risk RS (t1, p) at the time t1 of the target component p (S11, S12). Next, the failure risk prediction unit 4 calculates a failure risk trend curve RS (t, x, p) and a future failure risk RS (t + ΔT, x, y, p) (S13, S14). Whether maintenance / operation is to be changed automatically based on the calculated future failure risk, for example by comparing with a predetermined threshold, or by manual input from the maintenance / operator etc. Judging. If it is determined that the change is necessary, the physical quantity / condition to be changed to the maintenance / operation scenario formulation 12 automatically by a predetermined process or manually by an input from an input unit by a maintenance / operator or the like. Are set / input, and the maintenance / operation scenario formulation unit 3 formulates a maintenance / operation scenario according to the setting / input (S15, S16, S14). As described above, such a procedure is repeated at intervals of 10 minutes, for example, to assist the stable operation of the product.
［第２の実施形態］
 
　次に、図１３～１９を用いて、本実施形態の第２の実施形態による稼働補助システムについて説明する。
　第２の実施形態によれば、対象製品と同型の製品、類似の製品の信頼性の評価値を使って、製品を構成する複数部品の高精度な信頼性の評価値を考慮して、製品の運用、保守計画を実施可能とすることができる。 [Second Embodiment]

Next, an operation assistance system according to the second embodiment of the present embodiment will be described with reference to FIGS.
According to the second embodiment, the reliability evaluation value of the product of the same type as the target product and the reliability of the similar product is used, and the high-accuracy reliability evaluation value of a plurality of parts constituting the product is considered. The operation and maintenance plan can be implemented.
　本実施形態は、図１３に示すように、過去から現在に至る製品の環境データ、運転データ、設計データ、材料データの少なくともいずれかを含む情報に加え、製品１とその同型機、類似機１３の故障データの情報を利用し、故障リスク評価・更新部１４で故障リスクの評価と更新を実行するものである。製品１とその同型機、類似機１３を構成する複数の部品の故障データは故障データベース１５に蓄積される。ここで故障データとは、例えば運転開始から故障までの稼働時間や、運転開始から故障時までの環境データ、運転データ、保守・運用データを含んでいる。また同型機とは、製品１と同じ型式の製品で稼働している場所が異なる製品、類似機とは、製品１とは別の型式の製品を含んでいる。 In the present embodiment, as shown in FIG. 13, in addition to information including at least one of environmental data, operation data, design data, and material data of products from the past to the present, the product 1 and its similar machines, similar machines 13 The failure risk evaluation / update unit 14 executes failure risk evaluation and update using the failure data information. Failure data of a plurality of parts constituting the product 1 and its similar machines and similar machines 13 is stored in the failure database 15. Here, the failure data includes, for example, the operation time from the operation start to the failure, the environmental data from the operation start to the time of the failure, the operation data, and the maintenance / operation data. The same type machine includes products of the same type as that of the product 1, and the similar machine includes a product of a different type from the product 1.
　図１４は、製品に含まれる部品の応力履歴から余寿命評価で破壊確率を演算する場合において、等価応力振幅で寿命の確率密度関数を描き、それをベイズ統計に基づき更新する例を示す図である。
　故障リスク評価・更新部１４において、製品に繰り返し負荷が作用して発生する疲労現象を対象とし、余寿命評価で破壊確率Ｆ（ｔ１）を求める場合、過去から現時点ｔ１までに部品に発生した応力履歴にレインフロー法などの頻度解析法を適用して応力頻度分布を求める（図４参照）。実際、応力履歴には様々な大きさの応力が含まれるため、それを表現するために応力頻度分布２１が利用されるが、ある大きさの応力のみが発生したと仮定した場合の等価応力振幅Ｓｅｑを例えば図１４中の式を使って応力頻度分布から計算することができる。等価応力振幅Ｓｅｑが得られれば、等価応力振幅Ｓｅｑにおいて部品が破壊する寿命の確率密度関数１６を描くことができる。故障リスクの更新は、この破壊寿命の確率密度関数の更新によって実現できる。すなわち、故障データベース１５に含まれる故障データから計算した尤度と、更新前に利用していた事前の寿命の密度関数から次式のベイズの定理を活用して、故障データを考慮した更新後の寿命の密度関数を得ることができる。
　　
　更新後の密度関数＝尤度×事前の密度関数　・・・（８）
  FIG. 14 is a diagram showing an example in which the probability density function of the life is drawn with the equivalent stress amplitude and updated based on Bayesian statistics when the failure probability is calculated by the remaining life evaluation from the stress history of the parts included in the product. is there.
When the failure risk evaluation / update unit 14 targets fatigue phenomena that occur due to repeated loads on the product and obtains the failure probability F (t1) in the remaining life evaluation, the stress generated in the part from the past to the current t1 A stress frequency distribution is obtained by applying a frequency analysis method such as a rainflow method to the history (see FIG. 4). Actually, since the stress history includes stresses of various magnitudes, the stress frequency distribution 21 is used to express the stress, but the equivalent stress amplitude when only a certain magnitude of stress is assumed to be generated. Seq can be calculated from the stress frequency distribution using, for example, the formula in FIG. If the equivalent stress amplitude Seq is obtained, the probability density function 16 of the lifetime at which the part breaks at the equivalent stress amplitude Seq can be drawn. The failure risk can be updated by updating the failure life probability density function. That is, using the Bayes' theorem of the following equation from the likelihood calculated from the failure data included in the failure database 15 and the prior life density function used before the update, A lifetime density function can be obtained.

Density function after update = likelihood x prior density function (8)
　図１５に、このような破断寿命の密度関数の更新フローチャートを示す。まず、故障リスク評価・更新部１４は、対象部品ｐの過去から現時点ｔ１までの応力履歴を頻度解析して得られる応力頻度分布から、等価応力振幅Ｓｅｑ（ｐ）を図１４中の式を利用して計算する（Ｓ２１）。そして、故障リスク評価・更新部１４は、設計・材料データベース５等に予め記憶された対象部品ｐの材料データ（Ｐ－Ｓ－Ｎ線図）を参照し、等価応力振幅Ｓｅｑ（ｐ）での破断寿命の確率密度関数ｆ（Ｎ）を求める（Ｓ２２）。ここで、故障データベース１５に、対象部品ｐと同じ部品であり、同型機、類似機に搭載された部品ｐｊの故障データがｋ個存在するとする（ｊ＝１～ｋ）。故障リスク評価・更新部１４は、これらの部品の運転開始から故障時までの環境データ、運転データ、保守・運用データと設計・材料データから、故障時までの応力履歴および応力頻度分布を求め、図１５中の右側の式に従って等価応力振幅Ｓｅｑ（ｐｊ）での破断寿命Ｎｆ ｊを演算する（Ｓ２３）。故障リスク評価・更新部１４は、こうして求めたｋ個の破断寿命Ｎｆ ｊから、図１５中の左側の式に従って尤度Ｌを計算することができる（Ｓ２４）。つぎに、故障リスク評価・更新部１４は、事前の確率密度関数ｆ（Ｎ）と尤度Ｌから、式（８）に従って更新後の破断寿命の確率密度関数ｆ（Ｎ）’を得ることができる（Ｓ２５）。破壊寿命を扱う場合、一般に、事前の確率密度関数、尤度の確率分布形状はワイブル分布や対数正規分布となる。更新後の破断寿命の確率密度関数ｆ（Ｎ）’を参照すれば、更新後のＰ－Ｓ－Ｎ線図を描くことができる。
　つぎに、図４に示したように、過去から現時点ｔ１までに発生した応力履歴に頻度解析法を適用して得られる応力頻度分布と、予め定められた破壊確率Ｐ％の疲労寿命曲線から、疲労破壊Ｐ％に対する損傷度Ｄ（ｔ１）が次式で計算できる。
　
　Ｄ（ｔ１）＝（ｎ１／Ｎ１）＋（ｎ２／Ｎ２）＋…＋（ｎｍ／Ｎｍ）
　　　　　　　　　　　　　　　　　　　　　　　　　　　　・・・（２）
　
ここで、ｎ１、ｎ２、ｎｍはそれぞれ、応力履歴の頻度解析によって得られる応力振幅Ｓ１、Ｓ２，Ｓｍの繰り返し数である（ｍは整数）。また、Ｎ１，Ｎ２，Ｎｍはそれぞれ、応力振幅Ｓ１、Ｓ２，Ｓｍを繰り返して負荷した場合に、疲労破壊が破壊確率Ｐ％で発生する破断繰り返し数である。また、損傷度Ｄ（ｔ１）を発生させる繰り返し数Ｎ（ｔ１）は次式で求められる。
　　
　Ｎ（ｔ１）＝Ｄ（ｔ１）×Ｎｐ　・・・（３）
　
Ｎｐは予め定められた破壊確率Ｐ％の繰り返し数であり、応力振幅を予め定められた応力振幅Ｓi又はＳｅｑ（ｐ）に定めることで、図４中に示すように、Ｎ（ｔ１）と疲労寿命の確率密度関数から、図４の式により、破壊確率Ｆ（ｔ１）’を求めることができる。すなわち、製品の運転開始から現在までの経過時間をｔ１とすると、破壊確率Ｆ（ｔ１）’は密度関数ｆ（Ｎ）’を０からＮ（ｔ１）まで積分して得られる値である。
この更新後のＰ－Ｓ－Ｎ線図を利用して破壊確率を更新し、更新後の破壊確率Ｆ（ｔ１）’と影響度Ｃ（ｐ）を乗じることで、式（１）に従って更新後の故障リスクＲＳ（ｔ１，ｐ）’を演算することができる。このような応力頻度分布、Ｐ－Ｓ－Ｎ線図、故障データからの故障リスクの更新方法は一例であり、例えば等価応力振幅を介さずに更新後の破壊確率を演算しても良い。また、対象とする製品１と同型機、類似機１３の使用環境、運転状況、構造の類似度などに応じて、ベイズの定理に基づく破壊寿命の密度関数の更新式を変化させても良い。 FIG. 15 shows a flowchart for updating the density function of such a fracture life. First, the failure risk evaluation / update unit 14 uses the equation shown in FIG. 14 for the equivalent stress amplitude Seq (p) from the stress frequency distribution obtained by frequency analysis of the stress history from the past to the current time t1 of the target component p. To calculate (S21). Then, the failure risk evaluation / update unit 14 refers to the material data (PSN diagram) of the target part p stored in advance in the design / material database 5 or the like, and uses the equivalent stress amplitude Seq (p). A probability density function f (N) of the fracture life is obtained (S22). Here, it is assumed that there are k pieces of failure data of the parts p j that are the same parts as the target part p and mounted on the same type machine and similar machines in the failure database 15 (j = 1 to k). The failure risk evaluation / update unit 14 obtains the stress history and stress frequency distribution until failure from the environmental data, operation data, maintenance / operation data and design / material data from the start of operation to the failure of these parts, The fracture life N f j at the equivalent stress amplitude Seq (p j ) is calculated according to the expression on the right side in FIG. 15 (S23). The failure risk evaluation / updating unit 14 can calculate the likelihood L from the k pieces of the fracture life N f j thus obtained according to the expression on the left side in FIG. 15 (S24). Next, the failure risk evaluating / updating unit 14 can obtain the updated probability life function f (N) ′ of the fracture life from the probability density function f (N) and the likelihood L in advance according to the equation (8). Yes (S25). When dealing with the fracture life, generally the probability density function in advance and the probability distribution shape of likelihood are Weibull distribution or lognormal distribution. By referring to the probability density function f (N) ′ of the fracture life after the update, the PSN diagram after the update can be drawn.
Next, as shown in FIG. 4, from the stress frequency distribution obtained by applying the frequency analysis method to the stress history generated from the past to the present time t1, and the fatigue life curve of the predetermined failure probability P%, The degree of damage D (t1) with respect to fatigue failure P% can be calculated by the following equation.

D (t1) = (n 1 / N 1 ) + (n 2 / N 2 ) +... + (N m / N m )
... (2)

Here, n 1 , n 2 , and n m are respectively the number of repetitions of stress amplitudes S 1 , S 2 , and S m obtained by frequency analysis of stress history (m is an integer). N 1 , N 2 , and N m are the number of repetitions of fracture at which fatigue failure occurs with a failure probability P% when stress amplitudes S 1 , S 2 , and S m are repeatedly applied. Further, the number of repetitions N (t1) for generating the damage degree D (t1) is obtained by the following equation.

N (t1) = D (t1) × Np (3)

Np is the number of repetitions with a predetermined failure probability P%, and by setting the stress amplitude to a predetermined stress amplitude S i or Seq (p), as shown in FIG. 4, N (t1) From the probability density function of the fatigue life, the fracture probability F (t1) ′ can be obtained by the equation of FIG. That is, if the elapsed time from the start of operation of the product to the present is t1, the fracture probability F (t1) ′ is a value obtained by integrating the density function f (N) ′ from 0 to N (t1).
The updated probability is updated by using the updated PSN diagram, and the updated probability according to the formula (1) is obtained by multiplying the updated probability F (t1) ′ and the influence degree C (p). The failure risk RS (t1, p) ′ can be calculated. Such a method of updating the failure risk from the stress frequency distribution, PSN diagram, and failure data is an example. For example, the updated failure probability may be calculated without using the equivalent stress amplitude. In addition, the update function of the density function of the fracture life based on the Bayes' theorem may be changed according to the use environment of the same type machine as the target product 1, the operating environment of the similar machine 13, the operating situation, the structural similarity, and the like.
　また、図１６は、製品に含まれる部品の破壊寿命の確率密度関数に基づき破壊確率を演算する場合において、事前に設定した寿命の確率密度関数をベイズ統計に基づき更新する例を示す図である。
　故障リスク評価・更新部１４において、応力頻度分布やＰ－Ｓ－Ｎ線図は参照せず、図５の寿命の密度関数を直接利用して破壊確率を演算する場合も、式（８）に代表されるベイズの定理を利用することができる。すなわち、寿命の密度関数と故障データを式（８）に代入して寿命の密度関数を更新し、この更新後の確率密度関数で破壊確率Ｆ（ｔ１）’を求め（図１６）、それに影響度を乗じて故障リスクを更新することができる。ただし、製品１と同型機、類似機１３では使用環境や運転状況が大きく異なる場合、稼働時間が同じあっても製品１の部品と同型機、類似機１３の部品では破壊寿命が大きく異なると考えられる。そのような場合は、時間に加えて、破壊寿命に影響を及ぼす物理量を環境データ、運転データからいくつか選択して変数とし、多変量の寿命の確率密度関数を作成する。
　なお、寿命の確率密度関数ｆ（ｔ１）のデータは、例えば、対象製品毎に故障リスク評価部２の内部メモリ又は設計・材料データベース５等の適宜のメモリを予め記憶しておくことができる。また、寿命の確率密度関数ｆ（ｔ１）のデータは、環境データ、運転データ等を使用して、予め定めることができる。 FIG. 16 is a diagram illustrating an example of updating a preset probability density function of a lifetime based on Bayesian statistics when calculating a fracture probability based on a probability density function of a fracture life of a part included in a product. .
Even when the failure risk evaluation / update unit 14 calculates the failure probability by directly using the life density function of FIG. The representative Bayes theorem can be used. That is, the lifetime density function and the failure data are substituted into the equation (8) to update the lifetime density function, and the probability probability function after the update is used to determine the fracture probability F (t1) ′ (FIG. 16), which has an effect on it. The failure risk can be updated by multiplying the degree. However, if the operating environment and operating conditions of the same type machine and similar machine 13 are significantly different from those of the product 1, the breakdown life of the parts of the product 1 and the parts of the same type machine and similar machine 13 will be greatly different even if the operating time is the same. It is done. In such a case, in addition to time, a physical quantity that affects the fracture life is selected from environmental data and operation data as variables, and a multi-variable life probability density function is created.
Note that the data of the probability density function f (t1) of the lifetime can be stored in advance in an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product. Further, the data of the probability density function f (t1) of the lifetime can be determined in advance using environmental data, operation data, and the like.
　図１７は、時間を含む多変量の確率密度関数で破壊寿命を表し、破壊確率を演算する場合において、事前に設定した確率密度関数をベイズ統計に基づき更新する例を示す図である。
　図１７は、２変量の寿命の確率密度関数の例であり、この例では環境データあるいは運転データから選択した物理量ｘを変換した値ｘ’を時間とともに確率変数としている。この変換には、時間変化する物理量の統計量（ある時間間隔での平均値、最大値など）への変換や、頻度解析に基づく等価物理量への変換などが含まれている。寿命の密度関数が多変量であっても、図１７に示すように、１変量の場合と同様にベイズの定理に従って寿命の密度関数を更新することができる。また、破壊寿命に大きな影響を及ぼす物理量、あるいはそれを変換した物量ｘ’と時間ｔを含む関数ｒ（ｔ、ｘ’）を作成し、その関数を確率変数と解釈して破壊寿命の確率密度関数を作成することもできる。また、故障リスク評価・更新部１４において、予兆診断で破壊確率を演算する場合も、式（８）に代表されるベイズの定理を利用し、破壊の確率密度関数を更新すれば良い。この更新後の確率密度から破壊確率を求め、それに影響度を乗じて故障リスクを更新すれば良い。
なお、２変量の寿命の確率密度関数は、例えば、対象製品毎に故障リスク評価部２の内部メモリ又は設計・材料データベース５等の適宜のメモリを予め記憶しておくことができる。また、２変量の寿命の確率密度関数は、環境データ、運転データ等を使用して、予め定めることができる。 FIG. 17 is a diagram illustrating an example in which the failure life is expressed by a multivariate probability density function including time and the probability density function set in advance is updated based on Bayesian statistics when the failure probability is calculated.
FIG. 17 shows an example of a probability density function of a bivariate lifetime. In this example, a value x ′ obtained by converting a physical quantity x selected from environmental data or operation data is used as a random variable with time. This conversion includes conversion of a physical quantity that changes over time into a statistical quantity (average value, maximum value, etc. at a certain time interval), conversion into an equivalent physical quantity based on frequency analysis, and the like. Even if the life density function is multivariate, the life density function can be updated according to Bayes' theorem as in the case of univariate, as shown in FIG. In addition, a physical quantity that greatly affects the fracture life, or a function r (t, x ') including a quantity x ′ and a time t converted from the physical quantity is generated, and the probability density of the fracture life is interpreted by interpreting the function as a random variable. You can also create functions. Further, when the failure risk evaluation / update unit 14 calculates the failure probability by predictive diagnosis, the failure probability density function may be updated using the Bayes' theorem represented by the equation (8). A failure probability is obtained from the updated probability density, and the failure risk may be updated by multiplying it by the degree of influence.
Note that the probability density function of the bivariate lifetime can store, for example, an appropriate memory such as the internal memory of the failure risk evaluation unit 2 or the design / material database 5 for each target product. In addition, the probability density function of the bivariate lifetime can be determined in advance using environmental data, operation data, or the like.
　その後、故障リスク評価・更新部１４及び保守・運用シナリオ策定部３は、更新後の故障リスクＲＳ（ｔ１，ｐ）を用いて、図１２に示した第１の実施形態と同様に、故障リスクトレンド分析（Ｓ１３）、将来リスク予測（Ｓ１４）、保守・運用変更（Ｓ１５）、保守・運用シナリオ策定（Ｓ１６）の各処理を実行する。 Thereafter, the failure risk evaluation / update unit 14 and the maintenance / operation scenario formulation unit 3 use the updated failure risk RS (t1, p), as in the first embodiment shown in FIG. Each process of trend analysis (S13), future risk prediction (S14), maintenance / operation change (S15), and maintenance / operation scenario formulation (S16) is executed.
　本実施形態での製品１および同型機、類似機１３の故障データを利用した故障リスクの更新により、破壊確率の演算を高精度化できるので、製品１に含まれる部品の故障リスクの評価、予測をより高精度に実施することが可能となる。故障リスクの更新の時間間隔は、故障リスクの評価間隔と同じでも良いし、異なっていても良い。故障リスクの更新の頻度を多くすることで、より高精度に製品に含まれる部品の故障リスクを評価することができる。 By updating the failure risk using the failure data of the product 1, the same type machine, and the similar machine 13 in the present embodiment, the calculation of the failure probability can be made highly accurate, so that the failure risk evaluation and prediction of the parts included in the product 1 Can be carried out with higher accuracy. The time interval of failure risk update may be the same as or different from the failure risk evaluation interval. By increasing the frequency of updating the failure risk, it is possible to evaluate the failure risk of the parts included in the product with higher accuracy.
［第３の実施形態］
　次に、図１８、１９を用いて、本実施形態の第３の実施形態による稼働補助システムについて説明する。
 
　図１８は、本発明の第３の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図である。
本実施形態の保守・運用シナリオ策定部３では、製品１に実装されたセンサで計測する環境データ、運転データや、製品１についての設計データ、材料データ、保守・運用データに加えて、製品１に依存しない外部データベース１７からの情報が利用される。外部データベース１７に含まれる外部データとは、例えば大型計算機で計算された気象、海象の将来予測データや、資源の供給予測データ、資源の埋蔵予測データなどを含んでいる。このような外部データは、製品１の運転状態に影響されず、したがって外部データは製品１に依存するものではない。 [Third Embodiment]
Next, an operation assistance system according to the third embodiment of the present embodiment will be described with reference to FIGS.

FIG. 18 is a block diagram schematically showing main components of the operation assistance system according to the third embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationship.
In the maintenance / operation scenario formulation unit 3 of the present embodiment, in addition to the environmental data and operation data measured by the sensor mounted on the product 1, the design data, material data, and maintenance / operation data about the product 1, the product 1 Information from the external database 17 that does not depend on is used. The external data included in the external database 17 includes, for example, weather calculated by a large computer, sea state future prediction data, resource supply prediction data, resource reserve prediction data, and the like. Such external data is not affected by the operating state of the product 1, and therefore the external data does not depend on the product 1.
　図１９は、本発明の第３の実施形態による稼働補助システムの保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図である。
図１９のように、保守・運用シナリオ策定部３の故障リスク予測１１は、故障リスクのトレンドカーブ、保守・運用シナリオと外部データを利用して、将来の故障リスクを演算する。すなわち、環境データあるいは運転データに含まれる物理量ｘ、保守・運用データに含まれる物理量ｙ、外部データに含まれる物理量ｚから、故障リスクの予測値を次式で演算できる。
 
　ＲＳ（ｔ＋ΔＴ，ｙ，ｚ，ｐ）
　　　　　　＝ｇ５（ｔ＋ΔＴ，ｙ，ｚ，ｐ）　・・・（９）
　ＲＳ（ｔ＋ΔＴ，ｘ，ｙ，ｚ，ｐ）
　　　　　　＝ｇ６（ｔ＋ΔＴ，ｘ，ｙ，ｚ，ｐ）　・・・（１０）
 
外部データの利用により、製品１を取り巻く環境をより広く考慮することができるため、より精度良く将来の故障リスクを予測することができる。 FIG. 19 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit of the operation assistance system according to the third embodiment of the present invention and the flow of data exchanged between the components.
As shown in FIG. 19, the failure risk prediction 11 of the maintenance / operation scenario formulation unit 3 calculates a future failure risk using the failure risk trend curve, the maintenance / operation scenario, and external data. That is, the predicted value of failure risk can be calculated from the physical quantity x included in the environmental data or the operation data, the physical quantity y included in the maintenance / operation data, and the physical quantity z included in the external data by the following equation.

RS (t + ΔT, y, z, p)
= G5 (t + ΔT, y, z, p) (9)
RS (t + ΔT, x, y, z, p)
= G6 (t + ΔT, x, y, z, p) (10)

By using the external data, the environment surrounding the product 1 can be considered more widely, so that the future failure risk can be predicted with higher accuracy.
［第４の実施形態］
　次に、図２０、２１を用いて、本実施形態の第４の実施形態による稼働補助システムについて説明する。
 
　図２０は、本発明の第４の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図である。
　また、図２１は、本発明の第４の実施形態による稼働補助システムの保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図である。
本実施形態では製品１で計測した環境データ、運転データを破壊確率評価部１８に入力し、そこで製品１に含まれる部品の破壊確率Ｆを演算する。演算した破壊確率Ｆは、保守・運用シナリオ策定部３の入力となり、そこで破壊確率と影響度を乗じて故障リスクのトレンドカーブが作成される。 [Fourth Embodiment]
Next, an operation assistance system according to the fourth embodiment of the present embodiment will be described with reference to FIGS.

FIG. 20 is a block diagram schematically showing main components of an operation assistance system according to the fourth embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationships.
FIG. 21 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit of the operation assistance system according to the fourth embodiment of the present invention and the flow of data exchanged between the components.
In the present embodiment, the environmental data and operation data measured by the product 1 are input to the failure probability evaluation unit 18, where the failure probability F of the parts included in the product 1 is calculated. The calculated failure probability F becomes an input to the maintenance / operation scenario formulation unit 3, where a failure risk trend curve is created by multiplying the failure probability and the influence degree.
　図２２は、破壊確率のトレンドカーブを作成する場合の保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図である。
したがって、本実施形態では図２１のように、影響度データベース８は保守・運用シナリオ策定部３に配置される。一方、図２２に示すように、故障リスクのトレンドカーブではなく、破壊確率のトレンドカーブを作成する形態も考えられる。このような場合、部品ｐを対象とし、時間ｔと破壊確率又は破壊確率に影響を及ぼす物理量ｘから、次式のような破壊確率Ｆのトレンドカーブを作成できる。
 
　Ｆ（ｔ，ｐ）＝ｈ１（ｔ，ｐ）　・・・（１１）
　Ｆ（ｔ，ｘ，ｐ）＝ｈ２（ｔ，ｘ，ｐ）　・・・（１２）
 
式（１１）は時間ｔのみが破壊確率に影響を及ぼす場合、式（１２）は時間ｔと計測されている他の物理量ｘが破壊確率又は破壊確率に影響を及ぼす場合である。故障リスク予測１１では、保守・運用を異ならせた場合の将来の破壊確率Ｆを次式で計算する。
 
　Ｆ（ｔ＋ΔＴ，ｙ，ｐ）＝ｈ３（ｔ＋ΔＴ，ｙ，ｐ）　・・・（１３）
　Ｆ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）＝ｈ４（ｔ＋ΔＴ，ｘ，ｙ，ｐ）　
　　　　　　　　　　　　　　　　　　　　　　　　　　　・・・（１４）
 
ここで、ｙは保守・運転データのうち、故障リスクＲＳに影響を及ぼす物理量である。この将来の破壊確率又は破壊確率に影響度Ｃ（ｐ）を乗じて、次式のように将来の故障リスクＲＳを決定できる。
 
　ＲＳ（ｔ＋ΔＴ，ｙ，ｐ）＝Ｃ（ｐ）×ｈ３（ｔ＋ΔＴ，ｙ，ｐ）
　　　　　　　　　　　　　　　　　　　　　　　　　　　・・・（１５）
　ＲＳ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）
　　＝Ｃ（ｐ）×ｈ４（ｔ＋ΔＴ，ｘ，ｙ，ｐ）・・・（１６） FIG. 22 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit and the flow of data exchanged between the elements when creating a trend curve of the failure probability.
Therefore, in the present embodiment, as shown in FIG. 21, the impact database 8 is arranged in the maintenance / operation scenario formulation unit 3. On the other hand, as shown in FIG. 22, a form of creating a trend curve of failure probability instead of a trend curve of failure risk is also conceivable. In such a case, a trend curve of the failure probability F as shown in the following equation can be created for the part p from the time t and the physical quantity x that affects the failure probability or the failure probability.

F (t, p) = h1 (t, p) (11)
F (t, x, p) = h2 (t, x, p) (12)

Expression (11) is a case where only time t affects the destruction probability, and Expression (12) is a case where other physical quantity x measured as time t affects the destruction probability or destruction probability. In the failure risk prediction 11, the future failure probability F when the maintenance / operation is different is calculated by the following equation.

F (t + ΔT, y, p) = h3 (t + ΔT, y, p) (13)
F (t + ΔT, x, y, p) = h4 (t + ΔT, x, y, p)
(14)

Here, y is a physical quantity that affects the failure risk RS in the maintenance / operation data. The future failure risk RS can be determined by multiplying this future failure probability or failure probability by the degree of influence C (p) as shown in the following equation.

RS (t + ΔT, y, p) = C (p) × h3 (t + ΔT, y, p)
(15)
RS (t + ΔT, x, y, p)
= C (p) × h4 (t + ΔT, x, y, p) (16)
［第５の実施形態］
 
　図２３は、本発明の第５の実施形態による稼働補助システムの主な構成要素と、稼働補助システムで利用するデータを提供する製品およびデータベースと、それらの関係を概略示したブロック図である。
　以下に、図２３を用いて、本実施形態の第５の実施形態による稼働補助システムについて説明する。本実施形態では製品１で計測した環境データ、運転データを損傷度評価部１９に入力し、そこで製品１に含まれる部品の損傷度を演算する。損傷度は図４に示すように、応力履歴から余寿命評価で破壊確率を求める場合に、図３のＰ－Ｓ－Ｎ線図において、ある破壊確率の疲労寿命曲線を選択して求めることができる。演算した損傷度は、保守・運用シナリオ策定部３の入力となり、そこで材料データを参照して破壊確率を計算し、それに影響度を乗じて故障リスクのトレンドカーブが作成される。したがって、本実施形態では図２１のように、影響度データベース８は保守・運用シナリオ策定部３に配置される。一方、ここでも図２２に示すように、故障リスクのトレンドカーブではなく、破壊確率のトレンドカーブを作成する形態も考えられる。 [Fifth Embodiment]

FIG. 23 is a block diagram schematically showing main components of an operation assistance system according to the fifth embodiment of the present invention, products and databases that provide data used in the operation assistance system, and their relationships.
Below, the operation assistance system by 5th Embodiment of this embodiment is demonstrated using FIG. In the present embodiment, environmental data and operation data measured by the product 1 are input to the damage degree evaluation unit 19 where the damage degree of the parts included in the product 1 is calculated. As shown in FIG. 4, the degree of damage can be obtained by selecting a fatigue life curve with a certain probability of failure in the PSN diagram of FIG. it can. The calculated damage level is input to the maintenance / operation scenario formulation unit 3, where the failure probability is calculated by referring to the material data, and a failure risk trend curve is created by multiplying the damage probability. Therefore, in the present embodiment, as shown in FIG. 21, the impact database 8 is arranged in the maintenance / operation scenario formulation unit 3. On the other hand, as shown in FIG. 22, not only the failure risk trend curve but also a failure probability trend curve may be considered.
　さらに、図２４は、損傷度のトレンドカーブを作成する場合の保守・運用シナリオ策定部の主な構成要素と、要素間でやり取りされるデータの流れを概略示したブロック図である。図２４に示すように、損傷度のトレンドカーブを作成する形態も考えられる。すなわち、将来の損傷度ｄ３、ｄ４を作成し、影響度Ｃ（ｐ）と乗じて、次式のように故障リスクのトレンドカーブを決定することもできる。
 
　Ｆ（ｔ＋ΔＴ，ｙ，ｐ）
　　＝Ｋ（ｐ）×ｄ３（ｔ＋ΔＴ，ｙ，ｐ）・・・（１７）
　Ｆ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）
　　＝Ｋ（ｐ）×ｄ４（ｔ＋ΔＴ，ｘ，ｙ，ｐ）・・・（１８）
 
　ＲＳ（ｔ＋ΔＴ，ｙ，ｐ）
　　＝Ｃ（ｐ）×Ｋ×ｄ３（ｔ＋ΔＴ，ｙ，ｐ）・・・（１９）
　ＲＳ（ｔ＋ΔＴ，ｘ，ｙ，ｐ）
　　＝Ｃ（ｐ）×Ｋ×ｄ４（ｔ＋ΔＴ，ｘ，ｙ，ｐ）・・・（２０）
 
ここでＫ（ｐ）は、損傷度から破壊確率を計算するために必要な換算定数である。Ｋ（ｐ）は損傷度データベース、影響度データベースあるいは他のデータベースに蓄積されていても良い。本実施形態では、故障確率や損傷度が一度独立して演算されるため、故障確率や損傷度の可視化（表示部による表示）が容易になる利点がある。 FIG. 24 is a block diagram schematically showing the main components of the maintenance / operation scenario formulation unit when creating a trend curve of the degree of damage and the flow of data exchanged between the elements. As shown in FIG. 24, a form of creating a trend curve of the degree of damage is also conceivable. That is, the failure risk trend curve can also be determined as shown in the following equation by creating the future damage levels d3 and d4 and multiplying by the influence level C (p).

F (t + ΔT, y, p)
= K (p) × d3 (t + ΔT, y, p) (17)
F (t + ΔT, x, y, p)
= K (p) × d4 (t + ΔT, x, y, p) (18)

RS (t + ΔT, y, p)
= C (p) × K × d3 (t + ΔT, y, p) (19)
RS (t + ΔT, x, y, p)
= C (p) × K × d4 (t + ΔT, x, y, p) (20)

Here, K (p) is a conversion constant necessary for calculating the fracture probability from the degree of damage. K (p) may be stored in a damage degree database, an influence degree database, or another database. In the present embodiment, since the failure probability and the damage degree are independently calculated once, there is an advantage that the failure probability and the damage degree can be easily visualized (displayed by the display unit).
　式（１）に従って演算される故障リスクは、製品の稼働開始時から時刻ｔ１までの期間に、製品に累積した損傷を全て考慮した故障リスクである。一方、現時点ｔ１から将来のある時点ｔ１＋ΔＴまでの期間に、製品に累積した損傷のみを考慮した故障リスクを計算し、それを故障リスクの予測値とすることもできる。このような場合、まず現時点ｔ１から将来のある時点ｔ１＋ΔＴの期間に、製品に含まれるある部品が壊れる確率Ｐを次式で計算する。
 
　Ｐ（ｔ１，ｔ１＋ΔＴ）
　＝（Ｆ（ｔ１＋ΔＴ）－Ｆ（ｔ１））／（１－Ｆ（ｔ１））
　　　　　　　　　　　　　　　　　　　　　　　　　・・・（２１）
 
ここで、Ｆ（ｔ１）、Ｆ（ｔ１＋ΔＴ）はそれぞれ時刻ｔ１およびｔ１＋ΔＴでの破壊確率である。そして、現時点ｔ１から将来のある時点ｔ１＋ΔＴまでの期間の故障リスクは、対象部品の影響度Ｃ（ｐ），破壊確率Ｐ（ｔ１，ｔ１＋ΔＴ）から次式で計算できる。
 
　ＲＳ（ｔ１＋ΔＴ）＝Ｃ（ｐ）×Ｐ（ｔ１，ｔ１＋ΔＴ）
　　　　　　　　　　　　　　　　　　　　　　　　　・・・（２２）
 
式（２１）、（２２）の破壊確率、故障リスクは、環境データ、運転データ、保守・運用データに応じて変動するため、破壊確率、故障リスクは時間のみの関数ではないが、式（２１）、（２２）では簡単のために時間の関数として記述している。
  The failure risk calculated according to the equation (1) is a failure risk that considers all damage accumulated in the product during the period from the start of operation of the product to time t1. On the other hand, in the period from the current time t1 to a future time point t1 + ΔT, it is possible to calculate a failure risk considering only the damage accumulated in the product and use it as a predicted value of the failure risk. In such a case, first, a probability P that a certain part included in the product is broken is calculated by the following equation during a period from the current time t1 to a certain future time point t1 + ΔT.

P (t1, t1 + ΔT)
= (F (t1 + ΔT) −F (t1)) / (1−F (t1))
... (21)

Here, F (t1) and F (t1 + ΔT) are destruction probabilities at times t1 and t1 + ΔT, respectively. The failure risk during the period from the current time t1 to a future time point t1 + ΔT can be calculated from the influence degree C (p) and the failure probability P (t1, t1 + ΔT) of the target component by the following equation.

RS (t1 + ΔT) = C (p) × P (t1, t1 + ΔT)
(22)

Since the destruction probability and failure risk in the equations (21) and (22) vary depending on the environmental data, operation data, and maintenance / operation data, the destruction probability and failure risk are not functions of time alone. ) And (22) are described as a function of time for simplicity.
Ｃ．付記
 
　なお、本発明は上記した実施例に限定されるものではなく、様々な変形例が含まれている。例えば、上記した実施例は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施例の構成の一部を他の実施例の構成に置き換えることが可能であり、また、ある実施例の構成に他の実施例の構成を加えることも可能である。また、各実施例の構成の一部について、他の構成の追加・削除・置換をすることが可能である。
　また、上記の各構成、機能、処理部、処理手段等は、それらの一部又は全部を、例えば集積回路で設計する等によりハードウェアで実現してもよい。また、上記の各構成、機能等は、プロセッサがそれぞれの機能を実現するプログラムを解釈し、実行することによりソフトウェアで実現してもよい。各機能を実現するプログラム、テーブル、ファイル等の情報は、メモリや、ハードディスク、ＳＳＤ（Ｓｏｌｉｄ　Ｓｔａｔｅ　Ｄｒｉｖｅ）等の記録装置、または、ＩＣカード、ＳＤカード、ＤＶＤ等の記録媒体に置くことができる。
　また、制御線や情報線は説明上必要と考えられるものを示しており、製品上必ずしも全ての制御線や情報線を示しているとは限らない。実際には殆ど全ての構成が相互に接続されていると考えてもよい。 C. Appendix
In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.
Each of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files for realizing each function can be stored in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.
Further, the control lines and information lines indicate what is considered necessary for the explanation, and not all the control lines and information lines on the product are necessarily shown. Actually, it may be considered that almost all the components are connected to each other.
１　　　対象製品
２　　　故障リスク評価部
３　　　保守・運用シナリオ策定部
４　　　故障リスク予測部
５　　　設計・材料データベース
６　　　破壊確率計算
７　　　リスク計算
８　　　影響度データベース
１０　　故障リスクトレンド分析
１１　　故障リスク予測
１２　　保守・運用シナリオ策定
１３　　同型機、類似機
１４　　故障リスク評価・更新部
１５　　故障データベース
１６　　等価応力振幅での寿命の確率密度関数
１７　　外部データベース
１８　　破壊確率評価部
１９　　損傷度評価部
２０　　疲労寿命の確率密度関数
２１　　応力頻度分布
２２　　破壊確率Ｐ％の疲労寿命曲線
３０　　故障リスクデータベース
３１　　破壊確率データベース
３２　　損傷度データベース
１００　稼働補助システム
  DESCRIPTION OF SYMBOLS 1 Target product 2 Failure risk evaluation part 3 Maintenance / operation scenario formulation part 4 Failure risk prediction part 5 Design / material database 6 Failure probability calculation 7 Risk calculation 8 Influence database 10 Failure risk trend analysis 11 Failure risk prediction 12 Maintenance / operation scenario Formula 13 Similar machines, similar machines 14 Failure risk evaluation / update unit 15 Failure database 16 Life probability density function 17 with equivalent stress amplitude External database 18 Fracture probability evaluation unit 19 Damage degree evaluation unit 20 Fatigue life probability density function 21 Stress Frequency distribution 22 Fatigue life curve with failure probability P% 30 Failure risk database 31 Failure probability database 32 Damage degree database 100 Operation assistance system

Claims (15)

  1. 　稼働補助装置であって、
    　故障リスク評価部と、
    　保守・運用シナリオ策定部と、
    を備え、
     
    前記故障リスク評価部は、
    　対象製品の複数のセンサから入力された環境データ及び運転データと、予め定められた設計データ及び材料データを用いて、対象とする部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）を演算し、
    　時刻ｔ１において、製品に含まれる部品ｐの故障リスクＲＳ（ｔ１，ｐ）を、部品ｐの時刻ｔ１での破壊確率Ｆ（ｔ１）と、部品ｐが壊れた場合の予め定められた部品ｐ毎の影響度Ｃ（ｐ）との積により計算し、
     
    前記保守・運用シナリオ策定部は、
    　前記故障リスク評価部から送られてきた時刻ｔ１での部品ｐの故障リスクＲＳ（ｔ１、ｐ）と、前記故障リスク評価部から既に送られて故障リスクデータベースに記憶された過去から時刻ｔ１までの複数の故障リスクに基づき、対象製品から入力された環境データ及び運転データから予め選択された故障リスクに影響を及ぼす物理量ｘと、時間ｔとを変数として故障リスクのトレンドカーブを生成し、
    　故障リスクのトレンドカーブに基づき、現時点から予め定められた時間進んだ故障リスクの予測値を求め、
    　故障リスクの予測値に基づき、保守・運用者による入力部からの入力によりマニュアルで設定された又は予め定められた処理により自動で設定された製品の保守・運用シナリオに従い、時刻ｔと、物理量ｘと、保守データ及び／又は運転データから選択された故障リスクに影響を及ぼす物理量ｙとを変数とする故障リスク予測モデルを生成し、部品ｐの将来の故障リスクを予測し、
    　部品毎の将来の故障リスクを、故障リスクの予測値の高い順に整理して表示部に表示し及び／又は記憶部に記憶し、予め定められた複数の閾値でグループ分けし、保守・運用者による入力部からの入力によりマニュアルで又は予め定められた処理により自動で、グループ毎に各部品の保守時期を含む保守運用内容を設定して、保守運用内容を記憶部に記憶する及び／又は表示部に表示する、
    稼働補助装置。
          An auxiliary operation device,
    A failure risk assessment department;
    Maintenance and operation scenario development department,
    With

    The failure risk evaluation unit
    Using the environmental data and operation data input from a plurality of sensors of the target product, and predetermined design data and material data, the destruction probability F (t1) of the target component p at time t1 is calculated,
    At time t1, the failure risk RS (t1, p) of the component p included in the product is determined for each failure probability F (t1) of the component p at the time t1 and for each predetermined component p when the component p is broken. Calculated by the product of the degree of influence C (p)

    The maintenance / operation scenario formulation department
    The failure risk RS (t1, p) of the component p at time t1 sent from the failure risk evaluation unit, and the past from the past sent from the failure risk evaluation unit and stored in the failure risk database to time t1 Based on a plurality of failure risks, a trend curve of failure risk is generated with a physical quantity x that affects the failure risk selected in advance from the environmental data and operation data input from the target product and time t as variables,
    Based on the failure risk trend curve, obtain a predicted value of failure risk that has been advanced for a predetermined time from the present time,
    Based on the predicted value of failure risk, according to the product maintenance / operation scenario set manually by input from the input unit by the maintenance / operator or automatically set by a predetermined process, the time t and the physical quantity x And a failure risk prediction model with the physical quantity y affecting the failure risk selected from the maintenance data and / or operation data as variables, and predicting the future failure risk of the component p,
    The future failure risk for each part is arranged in order of the predicted value of failure risk, displayed on the display unit and / or stored in the storage unit, and grouped by a plurality of predetermined thresholds. The maintenance operation content including the maintenance time of each part is set for each group manually by input from the input unit or automatically by a predetermined process, and the maintenance operation content is stored in the storage unit and / or displayed. To display
    Operation assistance device.
  2. 　請求項１に記載の稼働補助装置において、
    　前記故障リスク評価部は、
    　環境データ、運転データおよび設計データを用いて、過去から現時点ｔ１までに部品に発生した応力履歴を計算し、
    　応力履歴に対して、レインフロー法又は他の頻度解析法を適用し、応力頻度分布を作成し、
    　応力頻度分布と、予め定められた疲労寿命曲線から損傷度Ｄ（ｔ１）を計算し、
    　損傷度Ｄ（ｔ１）を発生させる繰り返し数Ｎ（ｔ１）を求め、
    　材料データとして予め定められた疲労寿命の確率密度関数を０からＮ（ｔ１）まで積分して破壊確率Ｆ（ｔ１）を求める、
    ことを特徴とする稼働補助装置。
          The operation assistance device according to claim 1,
    The failure risk evaluation unit
    Using environmental data, operation data, and design data, calculate the history of stress generated in the part from the past to the present t1,
    Apply the rain flow method or other frequency analysis methods to the stress history, create a stress frequency distribution,
    Calculate the damage degree D (t1) from the stress frequency distribution and a predetermined fatigue life curve,
    The number of repetitions N (t1) for generating the damage degree D (t1) is obtained,
    Fracture probability F (t1) is obtained by integrating a probability density function of fatigue life predetermined as material data from 0 to N (t1).
    An operation assistance device characterized by that.
  3. 　請求項１又は２に記載の稼働補助装置において、
    　前記保守・運用シナリオ策定部は、
    　製品を分類した各コンポーネントに含まれるひとつ又は複数の部品を予め記憶したメモリを備え、
    　前記メモリを参照して、さらに、コンポーネントに対する各部品の将来の故障リスクの予測値を整理して表示部に表示する及び／又は記憶部に記憶することを特徴とする稼働補助装置。
          In the operation auxiliary device according to claim 1 or 2,
    The maintenance / operation scenario formulation department
    It has a memory that pre-stores one or more parts included in each component into which products are classified,
    The operation assisting apparatus, further comprising: referring to the memory, further arranging predicted values of a future failure risk of each part with respect to the component, displaying them on the display unit, and / or storing them in the storage unit.
  4. 　請求項１乃至３のいずれかに記載の稼働補助装置において、
    　製品に依存しない外部データを予め記憶した外部データベースを備え、
    　前記保守・運用シナリオ策定部は、故障リスクのトレンドカーブと、保守・運用シナリオと、外部データとを用いて、将来の故障リスクを演算することを特徴とする稼働補助装置。
          In the operation assistance apparatus in any one of Claims 1 thru | or 3,
    It has an external database that stores external data that does not depend on the product in advance.
    The maintenance / operation scenario formulation unit calculates a future failure risk using a failure risk trend curve, a maintenance / operation scenario, and external data.
  5. 　請求項１乃至４のいずれかに記載の稼働補助装置において、
    　前記保守・運用シナリオ策定部は、破壊確率Ｆのトレンドカーブ、及び／又は、損傷度のトレンドカーブを作成することを特徴とする稼働補助装置。
          In the operation auxiliary device according to any one of claims 1 to 4,
    The maintenance / operation scenario formulation unit creates a trend curve of the failure probability F and / or a trend curve of the degree of damage.
  6. 　風力発電システムであって、
    　請求項１乃至５のいずれかに記載の稼働補助装置と、
    　複数のセンサを有する、対象製品である風力発電機と、
    を備えた風力発電システム。
          A wind power generation system,
    The operation assisting device according to any one of claims 1 to 5,
    A wind power generator as a target product having a plurality of sensors;
    Wind power generation system equipped with.
  7. 　稼働補助装置であって、
    　故障リスク評価・更新部と、
    　保守・運用シナリオ策定部と、
    　対象製品とその同型機及び／又は類似機を構成する複数の部品の故障データを蓄積する故障データベースと、
    を備え、
     
    前記故障リスク評価・更新部は、
    　対象部品ｐが破壊する寿命の確率密度関数と、前記故障データベースに含まれる故障データから計算した尤度とからベイズの定理を活用して、故障データを考慮した更新後の寿命の確率密度関数を求め、
    　更新後の寿命の確率密度関数により、対象製品の複数のセンサから入力された環境データ及び運転データと、予め定められた設計データ及び材料データを用いて、対象とする部品ｐの時刻ｔ１での更新後の破壊確率Ｆ（ｔ１）’を演算し、
    　時刻ｔ１において、製品に含まれる部品ｐの更新後の故障リスクＲＳ（ｔ１，ｐ）’を、部品ｐの時刻ｔ１での更新後の破壊確率Ｆ（ｔ１）’と、部品ｐが壊れた場合の予め定められた部品ｐ毎の影響度Ｃ（ｐ）との積により計算し、
     
    前記保守・運用シナリオ策定部は、
    　前記故障リスク評価・更新部から送られてきた時刻ｔ１での部品ｐの更新後の故障リスクＲＳ（ｔ１、ｐ）’と、前記故障リスク評価・更新部から既に送られて故障リスクデータベースに記憶された過去から時刻ｔ１までの複数の更新後の故障リスクに基づき、対象製品から入力された環境データ及び運転データから予め選択された故障リスクに影響を及ぼす物理量ｘと、時間ｔとを変数として故障リスクのトレンドカーブを生成し、
    　故障リスクのトレンドカーブに基づき、現時点から予め定められた時間進んだ故障リスクの予測値を求め、
    　故障リスクの予測値に基づき、保守・運用者による入力部からの入力によりマニュアルで設定された又は予め定められた処理により自動で設定された製品の保守・運用シナリオに従い、時刻ｔと、物理量ｘと、保守データ及び／又は運転データから選択された故障リスクに影響を及ぼす物理量ｙとを変数とする故障リスク予測モデルを生成し、部品ｐの将来の故障リスクを予測し、予測値をトレンドカーブと共に記憶部に記憶する及び／又は表示部に表示する、
    稼働補助装置。
          An auxiliary operation device,
    Failure risk assessment / update department,
    Maintenance and operation scenario development department,
    A failure database for storing failure data of a plurality of parts constituting the target product and the same type machine and / or similar machine;
    With

    The failure risk assessment / update unit
    Using the Bayes' theorem based on the probability density function of the life of the target component p breaking and the likelihood calculated from the failure data included in the failure database, the probability density function of the updated life considering the failure data is obtained. Seeking
    Based on the probability density function of the lifetime after the update, the environment data and operation data input from a plurality of sensors of the target product and the design data and material data determined in advance are used at the time t1 of the target component p. Calculate the updated fracture probability F (t1) ′,
    When the failure risk RS (t1, p) ′ after the update of the part p included in the product is updated at the time t1, the failure probability F (t1) ′ after the update at the time t1 of the part p, and the part p is broken Calculated by the product of the degree of influence C (p) for each predetermined part p,

    The maintenance / operation scenario formulation department
    The failure risk RS (t1, p) ′ after the update of the component p at time t1 sent from the failure risk evaluation / update unit, and already sent from the failure risk evaluation / update unit and stored in the failure risk database Based on a plurality of updated failure risks from the past to time t1, the physical quantity x that affects the failure risk selected in advance from the environmental data and operation data input from the target product and the time t as variables Generate a trend curve of failure risk,
    Based on the failure risk trend curve, obtain a predicted value of failure risk that has been advanced for a predetermined time from the present time,
    Based on the predicted value of failure risk, according to the product maintenance / operation scenario set manually by input from the input unit by the maintenance / operator or automatically set by a predetermined process, the time t and the physical quantity x And a failure risk prediction model using the physical quantity y that affects the failure risk selected from the maintenance data and / or operation data as variables, predict the future failure risk of the component p, and use the predicted value as a trend curve. And store it in the storage unit and / or display it on the display unit,
    Operation assistance device.
  8. 　請求項７に記載の稼働補助装置において、
    前記故障リスク評価・更新部は、
    　部品ｐの過去から現時点ｔ１までの応力履歴を頻度解析して得られる応力頻度分布から、等価応力振幅Ｓｅｑ（ｐ）を、以下の式を用いて計算し、
     
    Figure JPOXMLDOC01-appb-M000001
      
    ni：応力振幅Siの頻度
    m：疲労寿命曲線の傾き
     
    　部品ｐの材料データを参照し、等価応力振幅Ｓｅｑ（ｐ）での破断寿命の確率密度関数ｆ（Ｎ）を求め、
    　前記故障データベースに、部品ｐと同じ部品であり、同型機及び／又は類似機に搭載された部品ｐｊの故障データがｋ個存在するとする（ｊ＝１～ｋ）と、これらの部品の運転開始から故障時までの環境データ、運転データ、保守・運用データ及び設計・材料データから、故障時までの応力履歴および応力頻度分布を求め、次式に従って等価応力振幅Ｓｅｑ（ｐ）での破断寿命Ｎｆ ｊを演算し、
     
    Figure JPOXMLDOC01-appb-M000002
      
    ni：応力振幅Siの頻度
    m：疲労寿命曲線の傾き
     
    　求めたｋ個の破断寿命Ｎｆ ｊから、次式に従って尤度Ｌを計算し、
     
    Figure JPOXMLDOC01-appb-M000003
      
    　事前の確率密度関数ｆ（Ｎ）と尤度Ｌから、次式に従って更新後の破断寿命の確率密度関数ｆ（Ｎ）’を得て、
     
    　　更新後の確率密度関数ｆ（Ｎ）’
    　　＝　尤度×事前の確率密度関数ｆ（Ｎ）
     
    　更新後の破断寿命の確率密度関数ｆ（Ｎ）’により更新後の破壊確率Ｆ（ｔ１）’を計算し、更新後の破壊確率Ｆ（ｔ１）’と、予め定められた部品ｐ毎の影響度Ｃ（ｐ）を乗じることで更新後の故障リスクＲＳ（ｔ１、ｐ）’を演算する、
    ことを特徴とする稼働補助装置。
          The operation assistance device according to claim 7,
    The failure risk assessment / update unit
    From the stress frequency distribution obtained by frequency analysis of the stress history of the part p from the past to the current t1, the equivalent stress amplitude Seq (p) is calculated using the following equation:

    Figure JPOXMLDOC01-appb-M000001
    n i : Frequency of stress amplitude S i
    m: slope of fatigue life curve
    With reference to the material data of the part p, the probability density function f (N) of the fracture life at the equivalent stress amplitude Seq (p) is obtained,
    If there are k pieces of failure data for the parts p j that are the same parts as the parts p and are mounted on the same type machine and / or similar machines in the failure database (j = 1 to k), the operation of these parts is performed. Stress history and stress frequency distribution from failure to environmental data from start to failure, operation data, maintenance / operation data and design / material data, and fracture life at equivalent stress amplitude Seq (p) according to the following equation N f j is calculated,

    Figure JPOXMLDOC01-appb-M000002
    n i : Frequency of stress amplitude S i
    m: slope of fatigue life curve
    The likelihood L is calculated according to the following equation from the obtained k pieces of fracture life N f j ,

    Figure JPOXMLDOC01-appb-M000003
    From the probability density function f (N) and the likelihood L in advance, the probability density function f (N) ′ of the fracture life after updating is obtained according to the following equation:

    Updated probability density function f (N) ′
    = Likelihood x prior probability density function f (N)

    The fracture probability F (t1) ′ after the update is calculated by the probability density function f (N) ′ of the fracture life after the update, and the fracture probability F (t1) ′ after the update and the influence for each predetermined part p. The updated failure risk RS (t1, p) ′ is calculated by multiplying the degree C (p).
    An operation assistance device characterized by that.
  9. 　請求項７又は８に記載の稼働補助装置において、
    　前記故障リスク評価・更新部は、
    　環境データ、運転データおよび設計データを用いて、過去から現時点ｔ１までに部品に発生した応力履歴を計算し、
    　応力履歴に対して、レインフロー法又は他の頻度解析法を適用し、応力頻度分布を作成し、
    　応力頻度分布と、予め定められた破壊確率Ｐ％の疲労寿命曲線から、疲労破壊Ｐ％に対する損傷度Ｄ（ｔ１）を計算し、
    　損傷度Ｄ（ｔ１）を発生させる繰り返し数Ｎ（ｔ１）を求め、
    　得られた更新後の疲労寿命の確率密度関数ｆ（Ｎ）’を０からＮ（ｔ１）まで積分して更新後の破壊確率Ｆ（ｔ１）’を求める、
    ことを特徴とする稼働補助装置。
     
          In the operation auxiliary device according to claim 7 or 8,
    The failure risk assessment / update unit
    Using environmental data, operation data, and design data, calculate the history of stress generated in the part from the past to the present t1,
    Apply the rain flow method or other frequency analysis methods to the stress history, create a stress frequency distribution,
    From a stress frequency distribution and a fatigue life curve with a predetermined failure probability P%, a damage degree D (t1) for fatigue failure P% is calculated,
    The number of repetitions N (t1) for generating the damage degree D (t1) is obtained,
    Integrating the obtained probability density function f (N) ′ of the updated fatigue life from 0 to N (t1) to obtain the updated fracture probability F (t1) ′.
    An operation assistance device characterized by that.

  10. 　請求項７乃至９のいずれかに記載の稼働補助装置において、
    　前記故障リスク評価・更新部は、
    　　予め記憶された寿命の確率密度関数ｆ（ｔ１）と前記尤度とによりベイズの定理を用い、寿命の密度関数を更新し、更新後の確率密度関数ｆ（ｔ）’を０からｔ１まで積分して更新後の破壊確率Ｆ（ｔ１）’を求め、それに影響度Ｃ（ｐ）を乗じて故障リスクを更新することを特徴とする稼働補助装置。
          In the operation auxiliary device according to any one of claims 7 to 9,
    The failure risk assessment / update unit
    Using the Bayesian theorem based on the probability density function f (t1) of the lifetime stored in advance and the likelihood, the lifetime density function is updated, and the updated probability density function f (t) ′ is integrated from 0 to t1. Then, the failure probability F (t1) ′ after the update is obtained, and the failure risk is updated by multiplying it by the influence degree C (p).
  11. 　請求項７乃至９のいずれかに記載の稼働補助装置において、
    　前記故障リスク評価・更新部は、
    　製品の計測データから破壊に関連する予め選択された複数の物理量に基づき、異常時の運転データ群による破壊の確率密度関数と前記尤度とによりベイズの定理を用い、破壊の確率密度関数を更新し、更新後の確率密度関数に対して、現時点の運転データの位置をプロットすることで、現時点ｔ１での更新後の破壊確率Ｆ（ｔ１）’を求めることを特徴とする稼働補助装置。
          In the operation auxiliary device according to any one of claims 7 to 9,
    The failure risk assessment / update unit
    Based on multiple pre-selected physical quantities related to destruction from product measurement data, update the probability density function of destruction using Bayes' theorem with the probability density function of destruction by the operation data group at the time of abnormality and the likelihood Then, the operation assisting device is characterized in that the updated destruction probability F (t1) ′ at the current time t1 is obtained by plotting the position of the current operation data with respect to the updated probability density function.
  12. 　請求項７乃至１１のいずれかに記載の稼働補助装置において、
    　製品に依存しない外部データを予め記憶した外部データベースを備え、
    　前記保守・運用シナリオ策定部は、故障リスクのトレンドカーブと、保守・運用シナリオと、外部データとを用いて、将来の故障リスクを演算することを特徴とする稼働補助装置。
          In the operation auxiliary device according to any one of claims 7 to 11,
    It has an external database that stores external data that does not depend on the product in advance.
    The maintenance / operation scenario formulation unit calculates a future failure risk using a failure risk trend curve, a maintenance / operation scenario, and external data.
  13. 　請求項４又は１２に記載の稼働補助装置において、
    　前記外部データは、予め計算された気象及び／又は海象の将来予測データ、資源の供給予測データ、資源の埋蔵予測データのいずれかひとつ又は複数を含むことを特徴とする稼働補助装置。
          In the operation auxiliary device according to claim 4 or 12,
    The operation assistance device according to claim 1, wherein the external data includes one or more of weather and / or sea state future prediction data, resource supply prediction data, and resource reserve prediction data calculated in advance.
  14. 　請求項７乃至１３のいずれかに記載の稼働補助装置において、
    　前記保守・運用シナリオ策定部は、破壊確率のトレンドカーブ、及び／又は、損傷度のトレンドカーブを作成することを特徴とする稼働補助装置。
          The operation assistance apparatus according to any one of claims 7 to 13,
    The maintenance / operation scenario formulation unit creates a trend curve of failure probability and / or a trend curve of damage degree.
  15. 　風力発電システムであって、
    　請求項７乃至１４のいずれかに記載の稼働補助装置と、
    　複数のセンサを有する、対象製品である第１の風力発電機と、
    　複数のセンサを有し、前記第１の風力発電機と同型機又は類似機の第２の風力発電機と、
    を備えた風力発電システム。
     
     
          A wind power generation system,
    The operation assisting device according to any one of claims 7 to 14,
    A first wind power generator as a target product having a plurality of sensors;
    A second wind power generator having a plurality of sensors, the same type as the first wind power generator, or a similar wind power generator;
    Wind power generation system equipped with.


PCT/JP2017/001621 2016-03-25 2017-01-19 Operation support device and wind power system WO2017163561A1 (en)

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