WO2024042733A1 - Système d'évaluation de probabilité de panne - Google Patents

Système d'évaluation de probabilité de panne Download PDF

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WO2024042733A1
WO2024042733A1 PCT/JP2023/001915 JP2023001915W WO2024042733A1 WO 2024042733 A1 WO2024042733 A1 WO 2024042733A1 JP 2023001915 W JP2023001915 W JP 2023001915W WO 2024042733 A1 WO2024042733 A1 WO 2024042733A1
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failure probability
data
failure
operation data
time
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Japanese (ja)
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将央 渡辺
寛 新谷
洋輔 植木
薫 網本
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株式会社日立製作所
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light
    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/20—Administration of product repair or maintenance

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  • the present invention relates to a technique for evaluating the failure probability of an object. Among these, it particularly relates to failure diagnosis and prediction (premonition), including calculation of failure probability.
  • the objects include equipment, facilities, mechanical systems, and parts that constitute these.
  • the mechanical systems perform their specified functions normally.
  • statistical analysis of maintenance records that have occurred in the past can be used to determine the number of corrective maintenance and preliminary maintenance that may occur in the future, and the remaining life of the target equipment. It is possible to predict.
  • the maintenance record refers to data in which the content of maintenance and the time of occurrence are recorded as a pair.
  • failure probability density function f(t) and failure probability function F are required to predict the number of corrective maintenance and preliminary maintenance that may occur in the future, and the remaining life of the target equipment.
  • (t), failure rate function ⁇ (t), etc. Techniques for this purpose are disclosed in Non-Patent Document 1 and the like.
  • t is the operating time of the equipment.
  • the failure probability density function f, the failure probability function F, and the failure rate function ⁇ are functions of the variable t, the respective relationships can be expressed by (Equation 1) and (Equation 2).
  • Non-Patent Document 1 assumes that the operating status of a mechanical system is a static status, that is, there is no variation in space and time, the operating status of a mechanical system is generally not constant in space and time.
  • the operating status of a wind power generator changes from moment to moment depending on the wind conditions, and the load varies depending on the location conditions.
  • the load on construction machinery and the like changes depending on the work environment, such as the work items that change from day to day, the driving characteristics of the driver, and the characteristics of the soil. Therefore, there is a limit to the accuracy of estimating the number of maintenance cycles and remaining life when evaluating a failure probability function or a failure rate function simply based on operating time.
  • Patent Document 1 evaluates the operating status that differs for each individual from time-series operating data measured by sensors, and takes this into account appropriately. This makes it possible to estimate the number of maintenance cycles and remaining life.
  • Patent Document 1 does not disclose a technique for pre-selecting only time-series operation data related to a failure. Therefore, an object of the present invention is to further improve the accuracy of estimation regarding maintenance of an object, such as the number of maintenance cycles and remaining life.
  • the present invention calculates the failure probability using operation data (for example, time-series operation data).
  • operation data for example, time-series operation data.
  • the present invention also includes a configuration that indicates the degree of dispersion of the failure probability function for calculating the failure probability, specifies the degree of dispersion according to the correlation with failure, and selects operation data accordingly. It will be done. More preferably, it is desirable to select operation data with a small degree of dispersion.
  • a more specific configuration of the present invention includes, in a failure probability evaluation system for evaluating the failure probability of failure in parts constituting a mechanical system, a maintenance history database that stores maintenance history data of the mechanical system; An operation database that stores a plurality of operation data indicating the operation status of parts, and a degree of variation in a failure probability function for calculating a failure probability for each operation data based on the operation data and maintenance history data, a dispersion degree calculation section that calculates a dispersion degree according to the correlation with failures in the parts; and an operation data selection section that selects operation data according to the calculated dispersion degree;
  • This is a failure probability evaluation system that implements evaluation of the failure probability by preferentially using data.
  • the present invention also includes a failure probability evaluation method using the failure probability evaluation system, a failure probability evaluation program that causes the failure probability evaluation system to function as a computer, and a storage medium that stores the same.
  • Example 1 Diagram showing an example of maintenance history data used in Example 1 A diagram showing an example of time-series operation data and a division flag table stored in the time-series operation database 2 used in the first embodiment.
  • Diagram showing an example of survival analysis data used in Example 1 A diagram showing the failure probability function and its coefficient of variation for the cumulative value of time-series operation data in Example 1 Flowchart for calculating the number of time-series operating data N s in Example 1 in Example 1 Graph showing the relationship between the deviation rate R and the number of selected time-series operation data Ns in Example 1 Graph showing the relationship between failure probability and cumulative damage in Example 1
  • GUI graphical user interface
  • a failure probability evaluation system 100 of the present embodiment includes a maintenance history database that stores maintenance history data of the mechanical system; An operation database that stores a plurality of operation data indicating the operation status of the component, and a degree of variation in a failure probability function for calculating a failure probability for each operation data based on the operation data and maintenance history data. , a dispersion degree calculation section that calculates a dispersion degree according to the correlation with failures in the parts, and an operation data selection section that selects operation data according to the calculated dispersion degree, The evaluation of the failure probability is realized by preferentially using operation data.
  • the failure probability of components of a mechanical system can be predicted with high accuracy by selecting operational data of sensors related to failure from a large amount of operational data and using it for failure probability evaluation. I can do it.
  • the failure probability evaluation system 100 is realized on a computer, and its functions are executed by a processing device according to a program.
  • the function may be realized by dedicated hardware, and the present invention is not limited to the use of a program (software).
  • the wind power generator 1 is used as a mechanical system, and the failure probability of the wind power generator 1 and its component parts is evaluated.
  • the application of the present invention is not limited only to the wind power generator 1.
  • FIG. 1 is a system configuration diagram in the first embodiment.
  • the failure probability evaluation system 100 of this embodiment includes a maintenance history database 3, a time-series operation database 2, a time-series operation data selection section 13, and a failure probability evaluation section 10. Note that the time-series operation database 2 and the maintenance history database 3 may be provided outside the failure probability evaluation system 100, or may be configured as a single database.
  • FIG. 2 is a diagram showing an example of maintenance history data 6 used in this embodiment.
  • the maintenance history data 6, as shown in FIG. 2 is data in which individuals in which maintenance has occurred are associated with dates and times when maintenance has occurred.
  • the site name and machine number to which the wind power generator 1, which is the object, belongs are used as information for identifying an individual. Note that an individual number or the like may be used as information for identifying an individual. This also applies to each data described below.
  • the maintenance history data 6 include a maintenance history including events (maintenance events) related to maintenance performed on the individual. As shown in FIG. 2, in this embodiment, each item of maintenance content, part name, and content is used as the maintenance history.
  • the maintenance content indicates the type of maintenance performed, and in this embodiment, "pre-maintenance” and “post-maintenance” are used.
  • Preliminary maintenance refers to maintenance such as replacing parts before a failure occurs, such as through periodic replacement.
  • Corrective maintenance refers to maintenance such as replacing parts after an abnormality or failure occurs.
  • the component name identifies the component or location that has been maintained or where an abnormality has occurred. Additionally, the content indicates the content of the maintenance performed.
  • the maintenance history data 6 may include the following items.
  • the reason for carrying out the maintenance can be used.
  • the occurrence events of failures, etc. can be used.
  • the wind power generator 1 is equipped with a function to automatically detect the maintenance history
  • the wind power generator 1 and the maintenance history database 3 are connected via a network, and the maintenance history data 6 is automatically transferred to the maintenance history database 3. May be accumulated.
  • the worker 4 in charge of maintenance may register the maintenance history in the maintenance history database 3. With this configuration, maintenance history including maintenance details for a plurality of component parts is accumulated in the maintenance history database 3.
  • time-series operation data 5 such as operation data regarding the components of the wind power generator 1 is accumulated in a time-series operation database 2 via a communication means such as a network.
  • the collection interval of each data is not particularly limited, it can be set to the maximum interval within a range that does not lose the amount of information about the target physical phenomenon. In this case, it is possible to collect meaningful data for failure probability evaluation while reducing data volume. For example, in the failure probability evaluation of the wind power generator 1 according to the present embodiment, since relatively long-term predictions such as several months or several years are handled, an interval of approximately one day is ideal. Note that the time-series operation data 5 is a type of operation data.
  • time-series operation data 5 may be measured values sampled at arbitrary intervals, it is more preferable to use statistical values such as the maximum value, minimum value, average value, and standard deviation within the collection interval. This makes it possible to make the most of information while significantly reducing the amount of data. For example, the average wind speed per day can be considered.
  • the information accumulated in the time-series operation database 2 is not limited to information obtained from the component parts themselves. For example, weather data such as temperature measured by weather observation equipment installed near the component is also useful in evaluating the load state of the component.
  • FIG. 3 is a diagram showing an example of the time-series operation data 5 and the division flag table 41 stored in the time-series operation database 2 used in this embodiment.
  • FIG. 3(a) is an example of the time-series performance data 5 output to the time-series performance data selection unit 13.
  • the time-series operation data 5 includes the following items: date and time, site name, machine number, and sensors 1 to N.
  • the date and time indicates the date and time when measurements were taken by sensors 1 to N.
  • the site name and machine number are the same as those in maintenance history data 6, and indicate the measurement targets of sensors 1 to N.
  • Sensors 1 to N indicate sensors that measure time-series operation data 5, respectively.
  • the average wind speed is measured as the time-series operation data 5 of the sensor 1.
  • other sensors 2 to N measure time-series operation data other than the average wind speed. That is, in this embodiment, N pieces of time-series operating data are measured by a total of N sensors 1 to N.
  • the data collection interval is one day. Note that here, for convenience, information other than information obtained directly from component parts, such as temperature measured by weather observation equipment, is also referred to as a sensor.
  • division flag table 41 Similar to general machine learning, failure probability evaluation devices need to have high prediction accuracy not only for the data used for learning but also for unknown data. In addition, in order to evaluate the prediction performance for unknown data, we divide the learning data used in the learning of the failure probability evaluation unit 10, which will be described later, and the test data used as a substitute for the unknown data, and calculate the failure probability for each. It is necessary to compare. Furthermore, in the division flag table 41, division flags are associated with each object (wind power generator 1). Specifically, in FIG. 3(b), the site name and machine number, which are examples of information for identifying the individual object, are used.
  • the time-series operation data 5 is assigned a division flag (learning or testing) according to the site name and machine number.
  • the time-series operating data 5 is divided into learning data and test data.
  • the division flag according to the individual number shown in FIG. 3(b) is applied to the time-series operation data 5 shown in FIG. 3(a).
  • the time-series operating data 5 is divided into learning data and test data.
  • FIG. 3(b) the maintenance history data 6 can also be divided into learning data and test data.
  • the division method is not particularly limited, but it is preferable to divide it so that there is no bias in the number of failure occurrences between learning and testing.
  • the time-series operating data selection unit 13 includes a feature quantity calculation unit 14, a failure correlation calculation unit 15, and a selected time-series operating data number determining unit 16. Further, the time-series operation data selection unit 13 selects time-series operation data of the sensor related to the failure from the time-series operation data 5 . Then, the time-series operation data selection unit 13 adds a division flag to the selected time-series operation data using the division flag table 41, and outputs this and the maintenance history data 6.
  • the time-series operation data selection unit 13 selects 10 pieces of time-series operation data of sensors related to the failure. Then, the time-series operation data selection unit 13 outputs the maintenance history data and the data set 9 and the data set 91 of the selected time-series operation data.
  • the data set 9 includes both learning data and test data
  • the data set 91 includes only learning data. Descriptions of the feature value calculation section 14, the failure correlation calculation section 15, and the selected time-series operation data number determining section 16, as well as a specific method for selecting time-series operation data of sensors related to failures, will be described later.
  • the failure probability evaluation section 10 includes a life modeling section 17, a life model recording section 18, and a failure probability output section 19.
  • the lifespan modeling section 17 receives the data set 91 (learning data), generates a lifespan model 20, and records it in the lifespan model recording section 18.
  • the failure probability output unit 19 receives the data set 9 (learning data, test data) as input, calls the life model 21 recorded in the life model recording unit 18, and outputs the failure probability 11.
  • the lifespan model 20 refers to a machine learning model, a statistical model, etc. that calculates failure probability from a selected set of time-series operation data. These models take time-series operation data (operation information) as input and output the corresponding failure probability. Below is a detailed explanation of each model.
  • a machine learning model is a model that uses machine learning methods such as neural networks (deep learning, etc.) and random forests to directly calculate failure probabilities up to the point of input based on time-series operating data (operating information). It is. Note that the machine learning method is not particularly limited to the above method. Parameters of the trained machine learning model (for example, weighting coefficients of neurons in each layer of the neural network) are recorded in the lifespan model recording unit 18.
  • a statistical model is a model that generates explanatory variables from a set of time-series operation data, learns the correspondence between the failure probability and explanatory variables using probability and statistical methods based on maintenance history data, and then operates over time based on the correspondence.
  • This is a model that calculates failure probability from data.
  • the parameters of the probability distribution are determined by maximum likelihood estimation, Bayesian estimation, or the like.
  • a non-parametric method such as the Kaplan-Meier method can be used. Note that the probability/statistical method is not particularly limited to the above method.
  • A is a constant corresponding to the deterioration reaction rate
  • R a is a gas constant
  • E a is activation energy.
  • the parameters ⁇ and ⁇ are the shape parameter and scale parameter of the Weibull distribution, respectively.
  • the life span model recording unit 18 records parameters A and E a of explanatory variables and the type of probability function used for identification (Weibull distribution in the above example) as a statistical model.
  • the function identified as the failure probability function includes, in addition to the Weibull distribution, a gamma distribution, a lognormal distribution, etc., but is not particularly limited to the above.
  • the square value of the average wind speed is proportional to the wind load applied to the blades and the like. Therefore, the cumulative value of the square value of the average wind speed may be used as the explanatory variable.
  • the cumulative values calculated using counting methods such as the rainflow method as explanatory variables to calculate the correspondence with the failure probability (objective variable).
  • a statistical model may be constructed to represent the The rainflow method is one of the methods of counting the stress frequency of a member subjected to repeated fluctuating loads, and is effective when the failure mechanism is fatigue fracture.
  • lifespan models Although specific examples of machine learning models and statistical models have been shown above as lifespan models, the learning method of lifespan models is not limited to the above.
  • the failure probability output unit 19 inputs the maintenance history data including learning data and test data, the selected data set 9, and the life model 21 stored in the life model recording unit 18. Then, the failure probability output unit 19 outputs the correspondence relationships F 1 and F 2 of the failure probabilities (outputs) to the time-series operation data (inputs) of the learning data and the test data.
  • failure probabilities can be directly output for the selected data set (input). Therefore, the above correspondence relationship is represented by a set of pairs of input and output values obtained for each individual.
  • the correspondence between the maintenance history data, the selected data set (input), and the failure probability (output) is the failure probability function for a parametric model, or the failure probability function for a nonparametric model. For example, it is represented by a Kaplan-Meier curve.
  • the feature calculation unit 14 receives as input the time-series operation data 5 and maintenance history data 6 of each sensor. Then, the feature amount calculation unit 14 calculates the feature amounts for these and outputs survival analysis data 7.
  • the method of calculating the feature amount is not particularly limited, but it is preferable to use the cumulative value of time-series operation data of each sensor. During use of the object or its constituent parts, damage may accumulate and failure may occur. In the case of such a failure, for example, a wear-out failure, it is considered that the cumulative value of time-series operation data has a correlation with the failure.
  • the cumulative value calculation method since damage accumulation is an irreversible process, it is desirable that the cumulative value also be a monotonically increasing value. Therefore, when the time-series operating data 5 is negative, it is desirable to convert it using a function that does not take a negative value, such as an absolute value function or a soft plus function, and then calculate the accumulation. Note that it is preferable that the feature calculation unit 14 specify a division flag for the input data using the division flag table 41 and include this in the survival analysis data 7.
  • the cumulative value can be made to increase monotonically. Further, depending on the failure mechanism, as explained in the life modeling section, even higher precision can be achieved by applying conversion or counting methods to the time-series operation data itself and then taking cumulative values. For example, physical knowledge regarding failure mechanisms can be incorporated by applying the Arrhenius formula to temperature data, the square value to average wind speed data, and the rainflow method to stress data. Note that in calculating the cumulative value, the above conversion and counting method, or a combination of other methods may be used.
  • FIG. 4 shows an example of the survival analysis data 7 used in this example.
  • the survival analysis data 7 is the cumulative value of the instantaneous values of the time-series operation data 5 in FIG. 3, that is, the cumulative value of the time-series operation data of each sensor at the time of measurement. recorded.
  • a survival analysis flag (survival or failure) is added to the survival analysis data 7 based on the maintenance history data 6 in accordance with a maintenance event.
  • survival analysis flags a survival flag is added in the case of preliminary maintenance, and a failure flag is added in the case of corrective maintenance.
  • the failure correlation calculation section 15 can function as a dispersion degree calculation section that indicates the degree of dispersion of the failure probability function and calculates the degree of dispersion according to the correlation with failures in parts.
  • the failure correlation calculation unit 15 inputs learning data whose division flag is “learning” in the survival analysis data 7, and calculates the time-series operation of each sensor using the feature calculated by the feature calculation unit 14. Calculate the correlation between data and failures, and the correlation between operating time and failures. Specifically, the failure correlation calculation unit 15 uses the cumulative value of time-series operation data of each sensor as the feature amount. Then, the failure correlation calculation unit 15 outputs a list 8 in which the dispersion of the failure probability function with respect to the cumulative value and the dispersion of the failure probability function based on the total operating time are estimated.
  • the failure correlation calculation unit 15 evaluates the failure probability function that matches the cumulative value and total operation time of the time-series operation data of each sensor of the survival analysis data 7 through the same process, and calculates the number of time-series operation data. (N)+1 failure probability functions can be obtained. Once the failure probability function is determined, its variation can be easily quantified using, for example, the coefficient of variation.
  • the mean E(x) and variance V(x) of the random variable x according to the failure probability function of the two-variable Weibull distribution shown in (Equation 4) can be calculated using the following (Equation 5) and (Equation 6).
  • FIG. 5 is a diagram showing the failure probability function and its coefficient of variation for the cumulative value of time-series operating data in this embodiment.
  • FIG. 5(a) shows a failure probability function 22 that matches the time-series operational data cumulative value (average wind speed cumulative value) of the sensor 1 related to the failure.
  • FIG. 5(b) shows a failure probability function 23 that is adapted to the cumulative value of time-series operating data of sensor N that is not related to failure.
  • FIG. 5C shows the failure probability function 24 based on operating time.
  • FIG. 5 shows a failure probability function 22 that matches the time-series operational data cumulative value (average wind speed cumulative value) of the sensor 1 related to the failure.
  • FIG. 5(b) shows a failure probability function 23 that is adapted to the cumulative value of time-series operating data of sensor N that is not related to failure.
  • FIG. 5C shows the failure probability function 24 based on operating time.
  • the failure probability function of the parts in the case of the wind power generator 1, is the cumulative value of the time-series operation data (average wind speed) of the sensor 1.
  • the probability of failure increases rapidly at locations where the value exceeds a certain value. This is because the average wind speed is a physical quantity that correlates with the wind load applied to the wind power generator.
  • Fig. 5 (a) shows that the variation in the failure probability function becomes smaller when the average wind speed is used as the standard. show.
  • the operating time-based failure probability function 24 is identified based on the cumulative value of operating hours (corresponding to the number of operating days in FIG. 4) without using time-series operating data. It is.
  • list 8 is shown in FIG. 5(d).
  • FIGS. 5(a) to 5(d) can be displayed on the display unit 12.
  • the selected time-series operating data number determining unit 16 includes the list 8, the maintenance history data 6, the correspondence relationship F 1 between the failure probability (output) and the time-series operation data (input) of the learning data, and the time-series operation data of the test data.
  • the correspondence relationship F 2 between failure probability (output) and (input) is input.
  • the selected time-series operating data number determining unit 16 outputs the maintenance history data 6 used for failure probability evaluation and a data set 9 of N s time-series operating data.
  • the selected time-series operating data number determination unit 16 can function as an operating data selection unit that selects operating data.
  • step S50 the time-series operational data selection unit 13 selects N s pieces of high-order time-series operational data with small variations in failure probability functions. This is done by deleting the columns of time-series operation data of each sensor shown in Figure 3 other than the selected time-series operation data of the top sensors with the smallest coefficient of variation, and only selecting the time-series operation data of the top sensors with the smallest variation. means to select.
  • An example of the maximum value N max of N s is the total number N of time-series operating data.
  • N s the maximum value of N s to the total number of time-series operation data corresponding to the feature quantity whose failure probability function has a smaller variation than the operation time.
  • the minimum value is 1.
  • the range of N s can be initially varied from 1 to N max at logarithmic intervals, and a rough search is performed to find a range that improves the predictive performance index described later, and then a detailed search is performed within that range in increments of 1. good.
  • the failure probability evaluation unit 10 inputs the selected time-series operation data and maintenance history data 6, and obtains the correspondence between the failure probability (output) and the time-series operation data (input). As a result, the failure probability evaluation unit 10 identifies the learning data correspondence relationship F 1 for the learning data, and the test data correspondence relationship F 2 for the test data.
  • the failure probability evaluation unit 10 calculates a predicted performance index of the life model from the correspondence relationship F 1 of the learning data and the correspondence relationship F 2 of the test data.
  • the above correspondence becomes a set of pairs of failure probabilities (outputs) for time-series operating data (inputs).
  • the degree of dissimilarity between sets may be calculated and used as an evaluation index. Examples of the degree of dissimilarity include Minkowski distance (eg, Euclidean distance and Manhattan distance), Mahalanobis distance, and cosine similarity multiplied by a negative value.
  • the above correspondence can be obtained as a failure probability function or Kaplan-Meier curve that defines the input-output relationship between time-series operating data (input) and failure probability (output). It will be done.
  • a failure probability function or a deviation rate R between Kaplan-Meier curves can be used as an evaluation index.
  • the deviation rate R can be calculated using (Equation 8). Note that this deviation rate R is an example of a degree of dispersion indicating a correlation.
  • the deviation rate R can be calculated by calculating the difference in failure probability at each point. Furthermore, when the failure probability function is obtained as a continuous probability distribution function, the Kullback-Leibler divergence D KL (F 1
  • the performance index of the lifespan model is not limited to the above-mentioned index.
  • the difference in the variation itself in the correspondence relationship of the failure probability (output) to the time-series operating data (input) for example, the coefficient of variation of the failure probability function F1 of the learning data and the failure probability function F2 of the test data
  • the difference in coefficient of variation can also be used.
  • steps S50 to S52 are repeated by changing the number N s of selected time-series operating data, and in step S53, the time-series operating data selection unit 13 determines the number N s of selected time-series operating data for which the evaluation index is the best. .
  • the number of time-series operating data measured by virtual sensors is set to 100.
  • the time-series operation data of 10 sensors are damage sensors related to the failure
  • 90 are the time-series operation data of dummy sensors not related to the failure.
  • Failures in the virtual data are generated according to the Weibull distribution of cumulative damage expressed as a linear sum of ten time-series operating data of the damage sensor.
  • the damage model D(x) obtained by the damage model generation/updating unit 303 in the failure probability evaluation unit 10, which will be described later with reference to FIG. 9, is used, which is desirable for this embodiment.
  • the failure probability evaluation unit identifies the Weibull distribution as the correspondence relationship between failure probabilities (output) with respect to time-series operating data (input) for learning and testing.
  • FIG. 7 is a graph showing the relationship between the deviation rate R and the number of selected time-series operating data N s in this embodiment.
  • the deviation rate R is plotted on the Y-axis
  • the number of selected time-series operating data N s is plotted on the X-axis.
  • the broken line in FIG. 7 is the deviation rate based on the total operating time, and the thick solid line indicates the deviation rate of 0. The closer the deviation rate is to 0, the better the prediction performance is, but in FIG. 7, the deviation rate is the smallest when the number of selected time-series operating data is 10. Therefore, 10 can be selected as the number N a of selected time-series operating data.
  • FIG. 8 is a graph showing the relationship between failure probability and cumulative damage in this example.
  • the failure probability function F 1 solid line A
  • the failure probability function F 2 broken line B
  • FIGS. 8(a) and 8(b) can be displayed on the display unit 12.
  • the selected time-series operation data number determining unit 16 determines the difference between the correspondence of operation history data during learning (for learning) and the correspondence between operation history data during testing (for testing).
  • the operation history data with the minimum rate (degree of deviation), that is, the minimum degree of deviation, is selected.
  • this correspondence relationship is a set of a predetermined number of pairs of time-series operational data and failure probabilities that are highly correlated with failures among the time-series operational data, or a parametric or non-parametric failure probability function. shown.
  • FIG. 9 is a system configuration diagram including a modification of the failure probability evaluation section 10 in this embodiment.
  • the time-series operation data selection unit 13 selects time-series operation data of sensors related to wear-out failures. Therefore, the failure probability evaluation unit 10 automatically generates an explanatory variable D(x t ) that appropriately expresses the cumulative damage to the equipment, and builds a statistical model for the explanatory variable to achieve higher effectiveness. can be expected.
  • the failure probability evaluation section 10 includes a life modeling section 17, a life model recording section 18, and a failure probability output section 19.
  • the life modeling section 17 includes an explanatory variable generation/updating section 30 and a failure probability function identification section 33.
  • the life modeling section 17 in this modification includes an explanatory variable generation/updating section 30 and a failure probability function identification section 33, and outputs an explanatory variable D(x).
  • the function for this can be realized by the technology described in Patent Document 1.
  • the explanatory variable D(X t ) is expressed as a function of the time-series operation data X t by (Equation 10).
  • d(x) is the damage to equipment per unit time
  • x is an operating data vector representing a time-series operating data set at a certain moment.
  • the time integral D(x) of damage d(x) per unit time is used as an explanatory variable that causes a device to fail.
  • the shape of the formula for calculating the explanatory variable D(x) is not particularly limited.
  • the explanatory variable D(x) can be expressed in the simplest form as a linear combination of the selected N s time-series operation data, and the optimization calculation can be performed at a relatively low calculation cost. It's over.
  • C (c1, c2, ... cNs) is a coefficient vector representing the weighting of each time series operation data
  • x (x 1, x 2, ..., xNs) is the coefficient vector representing the weighting of each time series operation data
  • x (x 1, x 2, ..., xNs) is the coefficient vector representing the weighting of each time series operation data. This is the moment-to-moment value of operating data.
  • failure probability function identification section 33 The processing of the failure probability function identification section 33, that is, the identification method of the failure probability function, is well-known like the failure probability output section 19 and the failure correlation calculation section 15.
  • the explanatory variable generating/updating section 30 minimizes variations in the failure probability function while repeatedly calling the failure probability function identification section 33.
  • the explanatory variable generation/update section 30 receives the time-series operation data and maintenance history data 6 used for failure probability evaluation as input, and outputs an explanatory variable D(x) and standard survival analysis data 32.
  • the explanatory variable-based survival analysis data 32 is the survival analysis data 7 to which a column of explanatory variables D(x) is added.
  • a failure probability function is obtained by identifying the failure probability function described above. Therefore, variations in the failure probability function, such as the coefficient of variation, can be easily evaluated.
  • the explanatory variable generation/updating unit 30 automatically searches for an explanatory variable that takes into account time-series operating data that minimizes the variation 31 of the failure probability function.
  • the explanatory variables obtained as a result are reflected in survival analysis data 7 to generate survival analysis data 32 based on the damage model.
  • the search for explanatory variables can be reduced to an optimization problem in which the objective function is varied and the explanatory variable D(x) is the parameter.
  • the failure mechanism is known to some extent, a method may be adopted in which only the shape of the equation is defined in advance by the user according to the failure mechanism, and the coefficients thereof are searched.
  • the conversion method and counting method as shown in the description of the life modeling section 17 and the feature value calculation section 14, and their combinations can be used.
  • the optimization calculation method for obtaining the coefficient vector representing the weighting of each time-series operational data is not particularly limited.
  • the objective function may be non-convex, it is desirable to use metaheuristics such as genetic algorithms or particle swarm optimization.
  • the explanatory variable D(x) learned as described above is stored in the life model recording section 18, and then called as appropriate by the failure probability output section 19 and used for calculating the failure probability.
  • the display unit 12 displays the failure probability up to the present time from the failure probability output unit 19, the variation in the failure probability function of the cumulative value of time-series operation data of each sensor, and the operation A list 8 that estimates the dispersion of the time-based failure probability function is input.
  • the display unit 12 then displays these.
  • the display unit 12 is specifically composed of a computer equipped with a screen drawing program and a display device, but the computer used here may have different functions from those of the arithmetic unit described above.
  • FIG. 10 is a diagram showing an example of the graphical user interface (GUI 40) on the display unit 12 in this embodiment.
  • the illustrated GUI 40 shows the correlation with the failure, the sensor number, the sensor name, and the coefficient of variation for time-series operation data that are highly relevant to the failure.
  • the correlation with the failure indicates the degree of association with the failure of the device, and the higher the rank, the higher the correlation.
  • the sensor number and sensor name indicate the sensor that measured the failure.
  • the coefficient of variation indicates the dispersion of the failure probability function. Therefore, in FIG. 10, the sensor numbers, sensor names, and coefficients of variation of the higher ranks (1st to 5th) that are highly relevant to failures from the time-series operation data are displayed.
  • the time-series performance data selected by the time-series performance data selection unit 13 is displayed on the screen of the display unit 12.
  • the user can quantitatively grasp information such as what kind of factor is the cause of component failure and how much the factor is related to the failure compared to the total operating time standard. Therefore, in this embodiment, it is possible to provide useful information not only for the maintenance and operation of the wind power generator 1 but also for the design and development of the wind power generator.
  • FIG. 11 is a system configuration diagram in the second embodiment.
  • the failure probability evaluation unit 10 predicts and evaluates future failure probabilities.
  • the difference from the failure probability evaluation unit 10 shown in FIG. 9 in the first embodiment is that it includes an operating status prediction unit 34.
  • the operating status prediction unit 34 receives the time-series operating data 5 and the learned life model 21 as input, and outputs an explanatory variable D(x) at a future point in time.
  • the method for calculating future failure probability is shown below. For the individuals alive at the present moment (t 0 ), the probability of failure after an arbitrary period of time is calculated separately. First, the predicted value 35 of the explanatory variable D(x) after an arbitrary time ( ⁇ t) has elapsed is predicted in advance by the operating status prediction unit 34. At this time, it is necessary to make predictions for each individual individually. The simplest method is to make an assumption that the average value of the values of the time-series operation data 5 recorded up to the present time will continue in the future. When using seasonally dependent operational data such as wind conditions and temperature, it is desirable to make estimates with reference to seasonal trends and forecasts from weather forecasting organizations.
  • time-series operation data may be arbitrarily generated for each of them.
  • time series prediction may be performed using a state space model such as a Kalman filter. Note that the specific prediction method is not limited to the above.
  • F(D) is the correspondence relationship of failure probability (output) to time-series operating data (input) identified as a failure probability function.
  • the failure probability is the expected value of the number of failure events occurring after an arbitrary time ( ⁇ t) has elapsed. The longer ⁇ t is, the larger this value will be, but it is usually desirable to set ⁇ t taking into account the periodic inspection interval of the mechanical system. .It is unlikely that the value will be close to 0. However, it is possible that the total value of failure probabilities for all individuals exceeds 1.0. This total value is the expected value of the number of failure event occurrences for the entire target individual.
  • the operation planning system 37 when the operation planning system 37 is connected to the wind power generator system, a method of changing the operation plan using failure probability may be adopted. For example, if the probability of failure is higher than expected until the periodic inspection scheduled for the future, it is possible to extend the life of the parts by changing the operation plan to actively stop the wind turbine or reduce output. It becomes possible. Due to this change in the operation plan, the future operation status will also change, and the future cumulative damage will also change, so in this case, the operation plan should be reflected in the calculation of future cumulative damage in the operation status prediction unit. is desirable. With such a configuration, the user can easily confirm the relationship between changes in the operation plan and changes in failure probability.
  • the calculation method in this example and the current and future failure probabilities calculated using this method can be used for management (inventory management and operation planning) of mechanical systems and facilities such as factories and plants other than wind power generator systems. Can be applied. An example of this application will be explained in Example 3.
  • Embodiment 3 applies the failure probability at a future point in time calculated in Embodiment 2 to machinery insurance.
  • a machinery insurance premium rate is determined using failure probability.
  • this embodiment is applied to insured equipment to construct a life prediction model.
  • machinery insurance rates based on this lifespan prediction model.
  • the lifespan prediction model is updated based on newly obtained time-series operation data and maintenance history data, and the model is updated.
  • the present invention may be applied not only to machinery insurance but also to insurance that covers fire, corrosion due to aging, rust, etc. in the category of failure.
  • FIG. 12 is a system configuration diagram in the third embodiment.
  • a failure probability evaluation system 100 excluding the display unit 12 is connected to an insurance company system 101 and an asset owner system 102 via a network 1000.
  • the insurance company system 101 and the asset owner system 102 have a network such as an intranet, and each information processing device (terminal or server) can access the failure probability evaluation system 100.
  • each information processing device terminal or server
  • it may be connected to the network 1000 without going through an intranet or the like. It is assumed that the display unit 12 is held by each of these terminals. Note that even when performing failure probability evaluation on a cloud system as described above, it is not always necessary to generate and update the damage model, and it may be performed as appropriate. Note that each of these devices can be realized by a so-called computer.
  • FIG. 13 is a hardware configuration diagram of the failure probability evaluation system 100 in the fourth embodiment.
  • the failure probability evaluation system 100 includes a processing device 111, a memory 112, a network interface 113, and a secondary storage device 114, which are connected to each other via a communication path such as a bus.
  • the processing device 111 is a so-called processor such as a CPU, and executes processing according to the failure probability evaluation program 115 stored in the secondary storage device 114. This process is the process of each part shown in Examples 1 to 3. Further, the memory 112 stores the failure probability evaluation program 115 used for processing in the processing device 111 and the above-mentioned data.
  • the secondary storage device 114 stores a failure probability evaluation program 115 and each of the above-mentioned data, that is, a time-series operation database 2 and a maintenance history database 3.
  • the secondary storage device 114 may be realized by various storage media such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a memory card.
  • the failure probability evaluation system 100 may be implemented in a separate device, such as a file server.

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Abstract

Le but de la présente invention est de sélectionner des données de fonctionnement en série chronologique associées à une panne à partir de données de fonctionnement en série chronologique mesurées par une grande quantité de capteurs, et de prédire plus précisément la durée de vie restante de composants constituant un système mécanique. La présente invention est un système d'évaluation de probabilité de panne pour évaluer la probabilité de panne de composants constituant un système mécanique, ledit système d'évaluation de probabilité de panne comprenant : une base de données d'historique de maintenance qui stocke des données d'historique de maintenance du système mécanique ; une base de données de fonctionnement qui stocke une pluralité d'ensembles de données de fonctionnement indiquant l'état de fonctionnement des composants ; une unité de calcul de degré de dispersion qui, sur la base de la pluralité d'ensembles de données de fonctionnement et des données d'historique de maintenance, calcule un degré de dispersion, qui indique le degré de variation d'une fonction de probabilité de panne pour calculer une probabilité de panne pour chaque ensemble de données de fonctionnement, et qui correspond à une corrélation avec des pannes des composants ; et une unité de sélection de données de fonctionnement qui sélectionne des données de fonctionnement selon le degré de dispersion calculé. Le système d'évaluation de probabilité de panne effectue l'évaluation de la probabilité de panne à l'aide, de préférence, des données de fonctionnement sélectionnées.
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JP2019160128A (ja) * 2018-03-16 2019-09-19 株式会社日立製作所 故障確率評価システム及び方法
JP2021028751A (ja) * 2019-08-09 2021-02-25 株式会社日立製作所 故障予兆診断システムおよび方法
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JP2021028751A (ja) * 2019-08-09 2021-02-25 株式会社日立製作所 故障予兆診断システムおよび方法
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