WO2018105104A1 - 故障リスク指標推定装置および故障リスク指標推定方法 - Google Patents
故障リスク指標推定装置および故障リスク指標推定方法 Download PDFInfo
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Definitions
- the present invention relates to a failure risk index estimation device and a failure risk index estimation method for estimating an index of a risk that a facility will fail.
- FMEA Failure mode effect analysis
- Items related to failures include, for example, the frequency of occurrence of failures in facilities and the magnitude of the impact of failures on facilities.
- the analysis result of FMEA may be referred to when evaluating this. is there.
- Patent Document 1 describes an operation rate prediction apparatus that predicts the operation rate of a prediction target machine system using FMEA.
- the operating rate prediction apparatus includes a map in which the correspondence relationship between the levels of a plurality of evaluation items related to the failure rate and the failure coefficient is defined. With reference to this map, the components of the prediction target machine system are configured. Specify the failure coefficient corresponding to the level of the evaluation item.
- the operation rate prediction apparatus estimates a failure rate of a component from the specified failure coefficient, and predicts an operation rate of the prediction target machine system based on the failure rate estimated for each of the plurality of components.
- the said map was fitted so that it might match the performance of the utilization rate of a similar mechanical system.
- the component rank coefficient of the component is selected in consideration of the failure factor of the component of the facility in the stopped state and the influence of the failure, and the component The degradation rank coefficient of the component is selected in consideration of the expected life.
- the movable management device calculates an inspection cycle expected for the component, and based on the calculated inspection cycle, the effective maintenance cycle of the component, that is, maintenance work. The interval is determined.
- the effective maintenance cycle is determined on the assumption that the inspection cycle expected for the component is a constant multiple of the current inspection cycle. If is not linear with respect to elapsed time, it will not hold. Since the risk of failure occurring in actual equipment generally changes nonlinearly, the movable management device cannot be applied to actual equipment as it is.
- the present invention solves the above-described problem, and provides a failure risk index estimation device and failure risk index estimation that can appropriately estimate an index of a risk that a facility will fail even when there is no or little maintenance data of the facility.
- the purpose is to obtain a method.
- the failure risk index estimation device includes a model formula construction unit and a parameter estimation unit.
- the model formula building unit changes the failure risk index according to the statistical distribution based on the information indicating the FMEA result for each inspection item of the parts constituting the facility and the information indicating the statistical distribution used for estimating the failure risk index. Is constructed.
- the parameter estimation unit determines the maintenance work interval and the default calculated from the model formula based on the information indicating the FMEA result for each inspection item of the parts constituting the equipment and the information indicating the maintenance operation interval predetermined for each inspection item of the part.
- the parameter value of the model formula that minimizes the difference from the performed maintenance work interval is statistically estimated.
- the model formula indicating the transition of the failure risk index according to the statistical distribution is constructed, and the parameter value of the model formula is statistically estimated as the estimated value of the failure risk index.
- FIG. 2A is a diagram showing items of information stored in an FMEA result database (hereinafter referred to as DB).
- FIG. 2B is a diagram illustrating items of information stored in the default work interval DB.
- FIG. 2C is a diagram illustrating statistical evaluation information.
- 3 is a diagram showing items of information stored in a first storage unit in Embodiment 1.
- FIG. 4A is a block diagram showing a hardware configuration for realizing the function of the failure risk index estimation apparatus according to Embodiment 1.
- 3 is a flowchart showing an operation of the failure risk index estimation device according to the first embodiment. It is a figure which shows the relationship between a failure risk parameter
- FIG. 13A is a diagram illustrating items of information stored in the facility information DB.
- FIG. 13B is a diagram illustrating items of information stored in the maintenance performance DB.
- FIG. 13C is a diagram illustrating items of information stored in the failure record DB.
- FIG. 14A is a diagram illustrating items of information stored in the narrowed-down data storage unit.
- FIG. 14B is a diagram illustrating items of information stored in the second storage unit.
- FIG. 14C is a diagram illustrating an estimated value of the failure risk index.
- 6 is a flowchart showing the operation of the failure risk index estimation device according to the second embodiment.
- 10 is a flowchart illustrating an operation of a narrowing unit according to the second embodiment.
- 10 is a flowchart illustrating an operation of an index estimation unit according to the second embodiment.
- 10 is a flowchart showing the operation of the merge unit in the second embodiment.
- FIG. 1 is a block diagram showing a functional configuration of a failure risk index estimation device 1 according to Embodiment 1 of the present invention.
- the failure risk index estimation apparatus 1 is an apparatus that estimates a failure risk index of equipment, and includes an FMEA result DB 2, a predetermined work interval DB 3, an index estimation unit 4, and a first storage unit 5.
- the index estimation unit 4 estimates a facility failure risk index based on the information read from each of the FMEA result DB2 and the predetermined work interval DB3 and the statistical evaluation information A.
- the failure risk index is information obtained by quantifying the magnitude of the risk that a failure will occur in the parts constituting the facility.
- FMEA result DB2 is a DB that stores FMEA results for each inspection item of the parts constituting the facility.
- information on items as shown in FIG. 2A is stored, for example.
- identification information of the component is set.
- identification information of the inspection item is set.
- the inspection items include, for example, appearance inspection, energization inspection, insulation inspection, and friction inspection. The current inspection and the insulation inspection both inspect the electrical continuity, and can be said to be similar inspection items.
- FMEA evaluation items are FMEA evaluation items.
- Frequency Frequency Level an FMEA evaluation level relating to the failure frequency is set.
- influence magnitude level an FMEA evaluation level relating to the magnitude of the influence of the failure on the part is set.
- detectability level an FMEA evaluation level relating to the ease of detecting a failure is set.
- the default work interval DB 3 is a DB that stores information indicating a maintenance work interval that is predetermined for each part inspection item.
- the default work interval DB3 for example, information on items as shown in FIG. 2B is stored.
- Part ID and “inspection item ID” are the same as those shown in FIG. 2A.
- number of predetermined work interval months the number of months of maintenance work intervals set for each inspection item of parts is set.
- the statistical evaluation information A is information indicating a statistical distribution used for statistical estimation of the failure risk index.
- item information as shown in FIG. 2C is stored.
- statistic distribution the type of statistical distribution used for statistical estimation of the failure risk index is set.
- Statistical distribution types include theoretically derived distributions such as Weibull distribution, gamma distribution, and lognormal distribution.
- a ratio of an error in which the estimated value of the failure risk index estimated by the index estimating unit 4 is allowed with respect to the standard deviation is set.
- the reliability rate of the failure risk index estimated by the index estimation unit 4 is set. Both the probability P1 of the evaluation result when the estimated value of the failure risk index falls within the allowable error and the probability P2 of the evaluation result when the estimated value of the failure risk index does not fall within the allowable error are both “reliable”
- the ratio ⁇ is equal to or greater than the value ⁇ (P1 ⁇ ⁇ , P2 ⁇ ⁇ ). Note that “allowable error” and “reliability” are information used in the merge process described later in the second embodiment, and thus the statistical evaluation information A in the first embodiment may not include these pieces of information. .
- the index estimation unit 4 includes a model formula construction unit 4a and a parameter estimation unit 4b.
- the model formula construction unit 4a constructs a model formula that indicates the transition (time change) of the failure risk index.
- the parameter estimation unit 4 b uses the above model formula so that the difference between the maintenance work interval calculated from the model formula and the predetermined maintenance work interval is minimized.
- the above model formula models the transition of the failure risk index according to the statistical distribution indicated by the statistical evaluation information A, and the parameter value of this model formula is the first estimated value.
- the first storage unit 5 stores a first estimated value that is a parameter value estimated by the index estimating unit 4. For example, information for each item as illustrated in FIG. 3 is stored in the first storage unit 5.
- Part ID and “Inspection item ID” are the same as those shown in FIG. 2A
- Statistical distribution is the same as that shown in FIG. 2C.
- a parameter that defines the statistical distribution used in the statistical estimation of the first estimated value is set.
- the shape parameter ⁇ and the scale parameter ⁇ of the cumulative density function that defines the Weibull distribution are set.
- time scale coefficient “Time scale coefficient”, “risk weight coefficient”, and “safety margin” are parameters of the above model formula, and parameter values estimated by the index estimation unit 4 are set.
- the “time scale factor” is a parameter related to the speed at which the failure risk increases in the above model formula.
- disk weight coefficient is a parameter related to the degree of weight of failure risk in the above model formula.
- the “safety margin” is a parameter indicating a time interval that goes back from the maintenance work date and time at the work interval estimated by the above model formula. With this parameter, parts maintenance work is executed ahead of schedule.
- FMEA result DB2 and default work interval DB3 are the database 100 shown in FIGS. 4A and 4B.
- Information stored in each of the FMEA result DB 2 and the default work interval DB 3 is input to the index estimation unit 4 through the DB input / output interface 101.
- the statistical evaluation information A is input to the failure risk index estimation device 1 through the information input interface 102.
- the estimated value of the failure risk index is output from the failure risk index estimation device 1 through the information output interface 103.
- the first storage unit 5 may be provided in a storage device in which the database 100 is provided, or may be provided in an internal memory of the processing circuit 104 illustrated in FIG. 4A.
- the first storage unit 5 may be provided in the memory 105 shown in FIG. 4B.
- Each function of the model formula construction unit 4a and the parameter estimation unit 4b in the failure risk index estimation device 1 is realized by a processing circuit. That is, the failure risk index estimation device 1 constructs a model formula indicating the transition of the failure risk index based on the FMEA result read from the FMEA result DB2 and the statistical evaluation information A, and the FMEA result DB2 and the predetermined work interval DB3. And a processing circuit for statistically estimating the parameter value of the model formula that minimizes the difference between the maintenance work interval calculated from the model formula and the predetermined maintenance work interval.
- the processing circuit may be dedicated hardware or a CPU (Central Processing Unit) that executes a program stored in the memory.
- CPU Central Processing Unit
- the processing circuit 104 is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, or an ASIC (Application Specific Integrated Circuit). , FPGA (Field-Programmable Gate Array), or a combination thereof.
- the functions of the model formula construction unit 4a and the parameter estimation unit 4b may be realized by separate processing circuits, or these functions may be realized by a single processing circuit.
- the processing circuit is a processor 106 as shown in FIG. 4B
- the functions of the model formula construction unit 4a and the parameter estimation unit 4b are realized by software, firmware, or a combination of software and firmware.
- Software or firmware is described as a program and stored in the memory 105.
- the processor 106 reads out and executes the program stored in the memory 105, thereby realizing the functions of the respective units. That is, the failure risk index estimation device 1 includes a memory 105 for storing a program in which step ST1 and step ST2 shown in FIG.
- these programs cause the computer to execute the procedures or methods of the model formula construction unit 4a and the parameter estimation unit 4b.
- the memory 105 includes, for example, a nonvolatile memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically-EPROM), or a volatile memory such as an EEPROM (Electrically-EPROM).
- a nonvolatile memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically-EPROM), or a volatile memory such as an EEPROM (Electrically-EPROM).
- a nonvolatile memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically-EPROM), or a volatile memory such as an EEPROM (Electrically-EPROM).
- EEPROM Electrically
- part of the functions of the model formula construction unit 4a and the parameter estimation unit 4b may be realized by dedicated hardware, and part of them may be realized by software or firmware.
- the function of the model formula construction unit 4a is realized by a processing circuit as dedicated hardware, and the function of the parameter estimation unit 4b is obtained by the processor 106 reading and executing a program stored in the memory 105. May be realized.
- the processing circuit can realize each of the above functions by hardware, software, firmware, or a combination thereof.
- FIG. 5 is a flowchart showing the operation of the failure risk index estimation apparatus 1 and shows a series of processing until the estimated value of the failure risk index is obtained and stored in the first storage unit 5.
- FIG. 6 is a diagram showing the relationship between the failure risk index R (t) and the intermediate evaluation indexes S, W, M, and shows the transition of the failure risk index R (t) in the elapsed time after the maintenance work.
- FIG. 7 is a diagram illustrating the relationship between the evaluation items C, E, and D of the FMEA result and the intermediate evaluation indexes S, W, and M.
- the operation of the failure risk index estimation apparatus 1 will be described along FIG. 5 with reference to FIGS. 6 and 7.
- the index estimation unit 4 constructs a failure risk index model expression based on the FMEA result read from the FMEA result DB2 and the statistical evaluation information A (step ST1).
- the failure risk index R (t) changes (changes in time) according to the statistical distribution f (t) as shown in the following formula (1).
- the time scale coefficient S is a coefficient for adjusting the change in the time direction of R (t), and is a parameter relating to the speed at which the failure risk increases.
- the risk weight coefficient W is a coefficient for adjusting the magnitude of R (t), and is a parameter related to the degree of weight of failure risk.
- R (t) W ⁇ f (S ⁇ t) (1)
- the index estimation unit 4 sets the safety margin M, and calculates the work interval T assumed from the model formula from the following formula (2).
- the time scale coefficient S, the risk weighting coefficient W, and the safety margin M are parameters that are assigned values until an estimated value of the failure risk index is obtained.
- these are called intermediate evaluation indexes.
- the statistical distribution f (t) can be expressed by a cumulative density function of the Weibull distribution shown in the following formula (3).
- the shape parameter ⁇ and the scale parameter ⁇ are statistical distribution parameters in the cumulative density function of the Weibull distribution.
- the above formula (1) can be expressed by the following formula (4) using the shape parameter ⁇ and the scale parameter ⁇ .
- f (t) 1 ⁇ exp ( ⁇ (t / ⁇ ) ⁇ ) (3)
- R (t) W ⁇ (1-exp ( ⁇ (S ⁇ t) ⁇ )) (4)
- T E (1 / S) ⁇ ( ⁇ ln (1 ⁇ (1 / W))) 1 / ⁇ (5)
- the FMEA result evaluation items include a failure frequency level, an impact magnitude level, and a detectability level.
- the failure frequency level is C
- the impact magnitude Let level be E and detectability level be D.
- the relationship shown in FIG. 7 exists between the evaluation item of the FMEA result and the intermediate evaluation index.
- the failure frequency level C can be an index of the frequency of occurrence of failures in the evaluation target component, and is therefore related to the time scale factor S related to the failure risk increase rate.
- the magnitude level E of the influence can be an index of the magnitude of the influence of the failure on the evaluation target part, the risk weight coefficient W relating to the degree of the weight of the failure risk and the safety margin M relating to the degree of advancement of the maintenance work. And both are related.
- the detectability level D can be an index of the ease of detecting a failure that has occurred in a part to be evaluated, and is therefore related to the safety margin M regarding the degree of advancement of maintenance work.
- the index estimation unit 4 statistically calculates the values of the parameters S, W, M, ⁇ , and ⁇ of the model formula so that the difference between the maintenance work interval T assumed from the model formula and the predetermined maintenance work interval T S is minimized. (Step ST2). Since the scale parameter ⁇ is fixed at “1”, the index estimation unit 4 calculates the values of the parameters S, W, M, and ⁇ of the model formula for each part and each inspection item, and uses them as the parts and the inspection items. The data is stored in the first storage unit 5 in association with each other.
- FIG. 8 is a flowchart showing the operations of the model formula construction unit 4a and the parameter estimation unit 4b.
- the model formula construction unit 4a merges the table data of the FMEA result DB2 and the default work interval DB3 (step ST1a).
- the table data is data composed of information for each item as shown in FIGS. 2A and 2B, and among the table data, the component ID, the inspection item ID, and the side-by-side information subsequent thereto are record data.
- the part ID, the inspection item ID, and the subsequent default work interval months are information constituting the table data.
- the model formula construction unit 4a reads the table data from the default work interval DB3, and searches the table data of the FMEA result DB2 having the same part ID and inspection item ID based on the part ID and inspection item ID in the table data.
- the model formula construction unit 4a executes a so-called merger that combines the FMEA results of the table data specified by this search with the table data read from the default work interval DB3.
- the model formula construction unit 4a performs the above process on all the table data in the predetermined work interval DB3, and generates information in which the predetermined maintenance work interval and the FMEA result are combined.
- This combination information is shown in FIG. 9, the default has been maintenance intervals T S, are expressed in months of working distance.
- table data that does not have the same component ID and inspection item ID as the table data in the default work interval DB3 is also added to the combination information shown in FIG.
- the model formula construction unit 4a assigns a parameter of an intermediate evaluation index related to the FMEA result according to the evaluation item and the evaluation level of the FMEA result for each component ID and each inspection item ID (step ST2a).
- the model formula construction unit 4a assigns a common intermediate evaluation index parameter to the FMEA result of the above combination information for the evaluation item related to the failure of FMEA and the evaluation level in this evaluation item is the same.
- Build a model formula The same value is set as the common parameter even in the above model formula between data having different part IDs and inspection item IDs. Thereby, it is possible to evaluate the influence of a failure that acts in common on different parts or inspection items.
- FIG. 10 is a diagram showing a result of assigning parameters of the intermediate evaluation index to the evaluation items of the FMEA result.
- Model expression construction unit 4a as shown in FIG. 10, for the parameters of the frequency level C Breakdown relevant time scale factor S, assigning the common parameter S 2.
- Model expression construction unit 4a as shown in FIG. 10, allocates the common parameters W 3 for the parameters of the risk weighting factor W that is relevant to the size level E of the impact.
- the detectability level D which is an evaluation item of the FMEA result, also has an evaluation level of “3”. For this reason, as shown in FIG. 10, the model formula construction unit 4 a assigns common parameters M 3 and 3 to the parameters of the safety margin M related to the detectability level D.
- the work interval T, the failure risk index R (t), and the appropriate interval TE are constructed as model equations for the failure risk index. Since the parameters of the intermediate evaluation index for the data of (EQ001, MT001) are S 2 , W 3 , M 3 , 3 , the model formula is as follows.
- a safety margin M may be expressed by the product M E ⁇ M D of the coefficient factor M D by detectable levels D and coefficient factor M E due to the size level E of the impact.
- the parameter estimation unit 4b calculates a working distance T from the model equation, the difference between the working distance T and default working interval T S is minimized on the basis of the parameter assigned by the model equation construction unit 4a
- Parameter estimation unit 4b for all the parts ID and inspection item ID in the combination information parameters S of the intermediate evaluation index as the sum of squares of the difference between the working distance T and the default is work interval T S is minimized, Estimate W, M and shape parameter ⁇ . As described above, the scale parameter ⁇ is “1”. As an estimation method of these parameters, for example, a conjugate direction method can be cited. However, as long as the method error between the default has been working distance T S can be used to estimate the parameters a minimum, but is not limited to conjugate vector method.
- the parameter estimation unit 4b classifies the results estimated as described above for each component ID and each inspection item ID, and stores them in the first storage unit 5 in association with the component ID and the inspection item ID (step ST4a). ). Since the statistical distribution f (t) follows the Weibull distribution, the “Statistic distribution” item shown in FIG. 3 is set to “Weibull distribution”, and the “Statistic distribution parameter” item includes the shape parameter ⁇ . Value is set.
- the parameter estimation unit 4b may use the information used in the statistical estimation of the parameter value for one of the inspection items similar to each other for the statistical estimation of the parameter value for the other of the inspection items. For example, since both the energization inspection and the insulation inspection inspect the electrical continuity state, they can be said to be similar inspection items. Therefore, the parameter estimation unit 4b uses the information used in the statistical estimation of the parameter value for the energization inspection for the statistical estimation of the parameter value for the insulation inspection. By doing in this way, the information used for statistical estimation can be reused and the processing load required for estimation can be reduced.
- the model formula construction unit 4a has a statistical distribution based on the FMEA result read from the FMEA result DB2 and the statistical evaluation information A. Build a model formula that shows the transition of the failure risk index. Based on the information read from the FMEA result DB2 and the default work interval DB3 by the parameter estimation unit 4b, the parameter value of the model formula that minimizes the difference between the maintenance work interval calculated from the model formula and the predetermined maintenance work interval Is estimated statistically. By configuring in this way, it is possible to appropriately estimate an index of the risk of failure of the equipment even when there is no or little equipment maintenance record data.
- the model formula construction unit 4a when the model formula construction unit 4a has the same evaluation item regarding FMEA failure and the evaluation level of the evaluation item, the model formula related to the evaluation item Assign common parameters to these parameters. By doing in this way, it is possible to evaluate the influence of the failure which acts in common on different parts or inspection items.
- the parameter estimation unit 4b uses the information used in the statistical estimation of the parameter value for one of the inspection items similar to each other for the other of the inspection items. Used for statistical estimation of the parameter values of By doing in this way, the information used for statistical estimation can be reused and the processing load required for estimation can be reduced.
- FIG. FIG. 12 is a block diagram showing a functional configuration of a failure risk index estimation device 1A according to Embodiment 2 of the present invention.
- the failure risk index estimation device 1A includes an equipment information DB 6, a maintenance performance DB 7, a failure performance DB 8, a narrowing unit 9, a narrowing data storage unit 10, an index estimation unit 11, and a second storage.
- the unit 12 and the merging unit 13 are provided.
- the equipment information DB 6 is a DB that stores equipment information including equipment, parts constituting the equipment, and inspection items for each part.
- the facility information DB 6 for example, information on items as shown in FIG. 13A is stored.
- equipment ID identification information of the facility is set.
- Part ID and “inspection item ID” are the same as those shown in FIG. 2A.
- maintenance start date and time the date and time when the maintenance contract for each part inspection item is started is set. It should be noted that the maintenance work date and time individually performed during the maintenance contract is the maintenance work execution date and time described later with reference to FIG. 13B.
- the maintenance result DB 7 is a DB that stores the result data of the maintenance work for each inspection item of the parts constituting the facility. Information on items as shown in FIG. 13B is stored in the maintenance result DB 7. In the “maintenance record ID” item, identification information of the record data of the maintenance work is set. The “equipment ID”, “part ID”, and “inspection item ID” are the same as those shown in FIGS. 13A and 2A. The maintenance work execution date for each part is set in the “maintenance work execution date” item.
- the failure result DB 8 is a DB that stores failure result data for each component constituting the facility. Information of items as shown in FIG. 13C is stored in the failure record DB 8. In the item of “failure record ID”, identification information of the record data of failure is set. “Equipment ID” and “component ID” are the same as those shown in FIG. 13B. In the item “Fault occurrence date and time”, the date and time when a fault has occurred in a component is set. In the item “related inspection item ID”, identification information of the inspection item related to the failure that has occurred is set.
- the narrowing-down unit 9 classifies the information stored in the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 into information corresponding to each FMEA result of the FMEA result DB 2. For example, the narrowing-down unit 9 aggregates the failure-free intervals after the maintenance work is performed for each maintenance result data, classifies the information indicating the total failure-free intervals for each FMEA result, and stores the information in the narrowed-down data storage unit 10. .
- the narrowing data storage unit 10 is a DB that stores information classified by the narrowing unit 9.
- the refined data storage unit 10 stores information on items as shown in FIG. 14A.
- “Frequency frequency level”, “Effect magnitude level”, and “Detectability level” are FMEA evaluation items, which are the same as those shown in FIG. 2A.
- Part ID” and “Inspection Item ID” are the same as those shown in FIG. 2A.
- “Maintenance work execution date / time” is the same as that shown in FIG. 13B.
- the item “No Failure Months” the number of months after maintenance work without failure and the next maintenance work, the number of months until a failure occurs after maintenance work, and up to the present without trouble after maintenance work
- One of the number of months is set.
- “Failure occurrence flag” a value indicating whether or not a failure has occurred in a component is set. For example, “1” is set when a failure occurs in a component, and “0” is set when no failure
- the index estimation unit 11 estimates the second estimated value of the failure risk index according to the relationship between the maintenance work result data and the failure result data, based on the information classified by the narrowing unit 9. For example, the index estimation unit 11 reads information corresponding to the FMEA result to be processed from the narrowed-down data storage unit 10, and statistically estimates a failure risk index for each part and each inspection item based on the read information. The estimated value of the failure risk index obtained by this estimation is stored in the second storage unit 12 together with the actual number of data used for the estimation.
- the second storage unit 12 stores the estimated value of the failure risk index estimated by the index estimation unit 11 for each part and each inspection item.
- the second storage unit 12 stores information on items as illustrated in FIG. 14B.
- “part ID” and “inspection item ID” are the same as those shown in FIG. 2A.
- “Statistical distribution” and “statistical distribution parameter” are the same as those shown in FIG.
- the “time scale coefficient”, “risk weight coefficient”, and “safety margin” are parameters that are the second estimated values of the failure risk index, and are the same as those shown in FIG.
- the item “number of actual data” the number of data used for statistical estimation of the failure risk index by the index estimation unit 11 is set.
- the merging unit 13 apportions the first estimated value estimated by the parameter estimating unit 4b and the second estimated value estimated by the index estimating unit 11, and calculates a final failure risk index estimated value. For example, the merging unit 13 calculates the first estimated value and the second estimated value according to the number of data assumed in the estimation of the first estimated value and the actual number of data used in the estimation of the second estimated value. Prorate and calculate the final failure risk index estimate. Information B indicating the estimated value of the failure risk index is output from the merging unit 13.
- the information B indicating the estimated value of the failure risk index is composed of information of items shown in FIG. 14C.
- “component ID” and “inspection item ID” are the same as those shown in FIG. 2A.
- “Statistical distribution” and “statistical distribution parameter” are the same as those shown in FIG.
- the “time scale coefficient”, “risk weight coefficient”, and “safety margin” are parameters that are the second estimated values of the failure risk index, and are the same as those shown in FIG.
- the FMEA result DB2, the default work interval DB3, the facility information DB6, the maintenance performance DB7, and the failure performance DB8 in the failure risk index estimation apparatus 1A are the database 100 shown in FIGS. 4A and 4B.
- Information stored in each of the FMEA result DB 2 and the default work interval DB 3 is input to the index estimation unit 4 through the DB input / output interface 101.
- Information stored in each of the facility information DB 6, the maintenance record DB 7 and the failure record DB 8 is input to the narrowing unit 9 through the DB input / output interface 101.
- the statistical evaluation information A is input to the failure risk index estimation apparatus 1A through the information input interface 102.
- Information B indicating the estimated value of the final failure risk index is output from the merging unit 13 through the information output interface 103.
- the first storage unit 5, the refined data storage unit 10, and the second storage unit 12 may be provided in a storage device in which the database 100 is provided, but may be provided in the internal memory of the processing circuit 104 illustrated in FIG. 4A. . Further, the first storage unit 5, the narrowed data storage unit 10, and the second storage unit 12 may be provided in the memory 105 illustrated in FIG. 4B.
- the failure risk index estimation device 1A includes a processing circuit for performing processing with the functions of the above-described units.
- the processing circuit may be dedicated hardware or a CPU that executes a program stored in a memory.
- the processing circuit 104 is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or these This is a combination.
- Each function of the index estimation unit 4, the narrowing-down unit 9, the index estimation unit 11, and the merging unit 13 may be realized by separate processing circuits, or these functions may be realized by a single processing circuit. .
- the functions of the index estimating unit 4, the narrowing unit 9, the index estimating unit 11 and the merging unit 13 are based on software, firmware, or a combination of software and firmware. Realized. Software or firmware is described as a program and stored in the memory 105. The processor 106 reads out and executes the program stored in the memory 105, thereby realizing the functions of the respective units. That is, failure risk index estimation device 1A includes memory 105 for storing a program that, when executed by processor 106, results in the processing from step ST1b to step ST8b shown in FIG. In addition, these programs cause the computer to execute the procedures or methods of the index estimation unit 4, the narrowing-down unit 9, the index estimation unit 11, and the merging unit 13.
- a part of each function of the index estimation unit 4, the narrowing unit 9, the index estimation unit 11, and the merging unit 13 may be realized by dedicated hardware, and a part may be realized by software or firmware.
- the function of the index estimation unit 4 and the narrowing-down unit 9 is realized by a processing circuit as dedicated hardware, and the processor 106 reads out the program stored in the memory 105 for the index estimation unit 11 and the merging unit 13.
- the function may be realized by executing the function.
- the processing circuit can realize each of the above functions by hardware, software, firmware, or a combination thereof.
- FIG. 15 is a flowchart showing the operation of the failure risk index estimation apparatus 1A, and shows a series of processes until the first estimated value and the second estimated value of the failure risk index are obtained and the final estimated value is output. ing.
- the narrowing-down unit 9 classifies the information stored in the facility information DB 6, the maintenance result DB 7 and the failure result DB 8 into information corresponding to each FMEA result (step ST1b). Information classified by the narrowing-down unit 9 is stored in the narrowed-down data storage unit 10.
- the index estimation unit 11 reads information corresponding to the FMEA result to be processed from the refined data storage unit 10, and statistically estimates a failure risk index for each part and each inspection item based on the read information (step ST2b). .
- the index estimation unit 11 stores the second estimated value and the actual number of data obtained by the above estimation in the second storage unit 12 in association with the component ID and the inspection item ID (step ST3b).
- step ST4b When all FMEA results stored in the FMEA result DB2 are not processed (step ST4b; NO), the processing from step ST1b is repeated. When all FMEA results stored in the FMEA result DB2 have been processed (step ST4b; YES), the process proceeds to step ST5b.
- step ST5b the index estimation unit 4 estimates the parameter value that is the first estimated value in the same manner as in the first embodiment.
- the index estimation unit 4 stores the estimated first estimated value in the first storage unit 5 in association with the component ID and the inspection item ID (step ST6b).
- the merging unit 13 reads the first estimated value stored in the first storage unit 5 and the second estimated value stored in the second storage unit 12, and reads the first estimated value and the first estimated value stored in the second storage unit 12.
- the estimated value of 2 is apportioned to calculate the final estimated value of the failure risk index (step ST7b). Thereafter, the merging unit 13 outputs information B indicating the estimated value of the calculated failure risk index (step ST8b).
- FIG. 15 shows the case where the estimation of the second estimated value by the index estimating unit 11 is performed prior to the estimation of the first estimated value by the index estimating unit 4, but the present invention is not limited to this. Absent.
- the estimation of the first estimation value by the index estimation unit 4 may be performed before the estimation of the second estimation value by the index estimation unit 11. Further, the estimation of the first estimated value by the index estimating unit 4 and the estimation of the second estimated value by the index estimating unit 11 may be performed in parallel.
- FIG. 16 is a flowchart showing the operation of the narrowing-down unit 9, and a series of processing until the information stored in the facility information DB 6, the maintenance result DB 7 and the failure result DB 8 is classified and stored in the narrowed-down data storage unit 10. Show. First, the narrowing-down unit 9 merges each table data of the FMEA result DB2, the facility information DB6, and the maintenance performance DB7 (step ST1c).
- the narrowing-down unit 9 searches the table data of the maintenance result DB 7 having the same equipment ID, part ID, and inspection item ID based on the equipment ID, part ID, and inspection item ID of the table data read from the equipment information DB 6. .
- the narrowing-down unit 9 merges the information set in the “maintenance work execution date / time” item of the table data identified by this search with the table data read from the facility information DB 6.
- the narrowing-down unit 9 merges without deleting the record data of the equipment ID, part ID, and inspection item ID used for the search when there is no maintenance result data of the same equipment ID, part ID, and inspection item ID as the equipment information. Leave in later table data.
- the narrowing-down unit 9 searches the table data of the FMEA result DB 2 having the same equipment ID, part ID, and inspection item ID based on the equipment ID, part ID, and inspection item ID in the merged table data.
- the narrowing-down unit 9 merges the FMEA result in the table data specified by this search into the table data after the merge.
- the narrowing-down unit 9 generates the information in which the facility information and the maintenance record data are classified for each FMEA result by executing the above processing on all the table data in the facility information DB 6.
- the narrowing-down unit 9 narrows down to only the record data corresponding to the FMEA result to be evaluated among the table data merged as described above (step ST2c).
- the narrowing-down unit 9 adds items of “number of months without failure” and “failure occurrence flag” to the record data of the merged table data.
- step ST3c the narrowing-down unit 9 performs the maintenance work for the part corresponding to the part ID based on the equipment ID and the part ID corresponding to the FMEA result to be evaluated until the next maintenance work.
- the failure record data that occurred earliest is retrieved from the failure record DB 8. If maintenance work has not been performed, the failure record data that has occurred earliest after the maintenance start date and time until the next maintenance work is searched.
- the narrowing-down unit 9 calculates the number of months without failure for which no failure has occurred in the part, and sets the calculated number of months as the item “number of months without failure”. Then, “1”, which is a value indicating that a failure has occurred, is set in the item of “failure occurrence flag”.
- the narrowing-down unit 9 calculates the time interval until the next maintenance work date and sets this number of months as the “no failure duration month” item, and a failure occurs. “0”, which is a value indicating that no failure has occurred, is set in the item of “failure occurrence flag”. If the date and time of the next maintenance work has not been determined, the interval up to the present time is set in the item “number of months without failure”. The narrowing-down unit 9 performs the above process on all the record data in the table data merged in step ST1c. Thereby, the information in which the facility information, the maintenance record data, and the failure record data are classified for each FMEA result is generated.
- the narrowing-down unit 9 stores the processing result table data in the narrowed-down data storage unit 10 (step ST4c).
- the table data includes “failure frequency level”, “effect level”, “detectability level”, “facility ID”, “part ID”, and “inspection item ID”. , “Maintenance work execution date and time”, “no failure duration months”, and “failure occurrence flag”.
- FIG. 17 is a flowchart showing the operation of the index estimation unit 11, and shows a series of processes from estimating the second estimated value to storing it in the second storage unit 12.
- the index estimation unit 11 searches the table data stored in the narrowed-down data storage unit 10 for record data having the same processing target component ID and inspection item ID.
- the number of record data calculated in this way is the total number of parts corresponding to the number of months elapsed after the maintenance work.
- the index estimation unit 11 searches record data in which “1” is set in the item of “failure occurrence flag” from the table data stored in the refined data storage unit 10. Based on the retrieved record data, the index estimation unit 11 calculates the number of failures corresponding to the number of months elapsed after maintenance work for each component ID and each inspection item ID (step ST2d). Next, the index estimation unit 11 calculates an actual failure rate which is a value obtained by dividing the number of failures according to the number of months elapsed after the maintenance work by the total number (step ST3d).
- the index estimation unit 11 identifies a statistical distribution that approximates the transition of the actual failure rate calculated in step ST3d, and statistically estimates the parameter value of the model formula according to this statistical distribution (step ST4d). For example, the time scale factor S and the shape parameter ⁇ that are approximated by the failure risk index R (t) when the transition of the actual failure rate is set to the risk weighting factor W and 1 in the above equation (4) are estimated.
- a statistical distribution such as a gamma distribution or a lognormal distribution may be used as long as it is a statistical distribution that minimizes an error from the actual data.
- the index estimation unit 11 checks whether or not there is an unprocessed combination among the combinations of the component ID and the inspection item ID of the table data stored in the refined data storage unit 10 (step ST5d). When there is an unprocessed combination among the combinations of the part ID and the inspection item ID of the table data stored in the narrowed-down data storage unit 10 (step ST5d; NO), the process returns to step ST1d and the above-described processing is repeated. When all combinations of the component ID and the inspection item ID are processed (step ST5d; YES), the index estimation unit 11 associates the second estimated value, which is the estimated parameter value, with the component ID and the inspection item ID. It memorize
- FIG. 18 is a flowchart showing the operation of the merging unit 13 and shows a series of processes until the final failure risk index estimated value is calculated and output from the first estimated value and the second estimated value. ing.
- the merging unit 13 reads the component ID and inspection item ID to be processed, the second estimated value and the actual number of data corresponding to these from the second storage unit 12 (step ST1e).
- the merging unit 13 reads out the processing target component ID and inspection item ID and the first estimated value corresponding to these from the first storage unit 5 (step ST2e).
- the record data that does not have is read from the first storage unit 5 as it is.
- N I is the number of data expected by statistical estimation of the first estimate Obtained (step ST3e).
- merging unit 13 uses the allowable error ⁇ and reliability index ⁇ in the statistical evaluation information A, calculates a N I from the following equation (7).
- N I (((4 / ⁇ 2 ) + (1/2)) ⁇ z ( ⁇ ) 2 ) (7)
- the merging unit 13 indicates the failure risk index R A (t) indicated by the record data read from the second storage unit 12 and the record data having the same component ID and inspection item ID read from the first storage unit 5
- the failure risk index R I (t) is apportioned (step ST4e).
- Combining unit 13, the total number of parts of each time after maintenance and N A + N I, the failure number N A ⁇ R A (t) + N I ⁇ R I (t) as the failure risk indicator R F ( t) is again statistically estimated.
- the estimation method for example, a conjugate direction method is used. The process so far is the apportionment process.
- N I is the number of data is assumed a statistical estimation of the first estimate proportional division of the second estimate. Thereby, the 1st estimated value and the 2nd estimated value can be apportioned appropriately.
- the merging unit 13 checks whether or not there is a combination of the component ID and the inspection item ID that are not apportioned among the information stored in each of the first storage unit 5 and the second storage unit 12 (step) ST5e). Among the information stored in each of the first storage unit 5 and the second storage unit 12, if there is a combination of a component ID and an inspection item ID that are not apportioned (step ST ⁇ b> 5 e; NO), the unprocessed combination On the other hand, the processing from step ST1e is repeated. When the combinations of all component IDs and inspection item IDs are apportioned (step ST5e; YES), the merging unit 13 indicates the final failure risk index estimated value from the parameter value of the failure risk index R F (t). Information B is generated and output (step ST6e).
- the narrowing-down unit 9 corresponds to the information stored in each of the facility information DB 6, the maintenance performance DB 7, and the failure performance DB 8 for each FMEA result. Classify it into information. Based on the information classified by the narrowing-down unit 9, the index estimation unit 11 estimates an estimated value of the failure risk index corresponding to the relationship between the maintenance work result data and the failure result data. The merging unit 13 apportions the first estimated value estimated by the index estimating unit 4 and the second estimated value estimated by the index estimating unit 11 to calculate the final estimated value of the failure risk index. . By comprising in this way, even if there is little maintenance performance data of an installation, the parameter
- the merging unit 13 uses the number of data assumed in the statistical estimation of the first estimated value for apportionment with the second estimated value. By comprising in this way, a 1st estimated value and a 2nd estimated value can be apportioned appropriately.
- the failure risk index estimation apparatus can appropriately estimate an index of risk of failure of equipment even when there is no or little maintenance data on the equipment, and can be applied to various mechanical systems, for example. It is.
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Abstract
Description
また、上記稼働率予測装置では、評価項目の互いに異なるレベル間で故障率の大小関係しか考慮されていない。例えば、故障の頻度レベルが高い故障が発生する故障率よりも、故障の頻度レベルが低い故障が発生する故障率が低くなるようにする制約条件のもとで、稼働率が予測されている。このため、実際の機械システムにおける故障率の変動を適切に評価できない。
実際の設備で故障が発生するリスクは一般に非線形に推移するため、上記可動管理装置を実際の設備にそのまま適用することはできない。
実施の形態1.
図1は、この発明の実施の形態1に係る故障リスク指標推定装置1の機能構成を示すブロック図である。故障リスク指標推定装置1は、設備の故障リスク指標を推定する装置であり、FMEA結果DB2、既定作業間隔DB3、指標推定部4および第1の記憶部5を備える。指標推定部4は、FMEA結果DB2および既定作業間隔DB3のそれぞれから読み出した情報と統計評価用情報Aとに基づいて設備の故障リスク指標を推定する。故障リスク指標とは、設備を構成する部品に故障が発生するリスクの大きさを定量化した情報である。
“信頼率”の項目には、指標推定部4により推定された故障リスク指標の信頼率が設定される。故障リスク指標の推定値が許容誤差以内に収まると評価された結果が外れる確率P1と、故障リスク指標の推定値が許容誤差に収まらないと評価された結果が外れる確率P2との両方が“信頼率”に設定された値α以上となる(P1≧α、P2≧α)。
なお、“許容誤差”と“信頼率”は、実施の形態2で後述する併合処理に用いる情報であるため、実施の形態1における統計評価用情報Aには、これらの情報がなくてもよい。
モデル式構築部4aは、FMEA結果DB2から読み出したFMEA結果と統計評価用情報Aとに基づいて、故障リスク指標の推移(時間変化)を示すモデル式を構築する。
パラメータ推定部4bは、FMEA結果DB2と既定作業間隔DB3とから読み出した情報に基づいて、モデル式から算出した保守作業間隔と既定された保守作業間隔との差分が最も小さくなるように上記モデル式のパラメータ値を統計的に推定する。
上記モデル式は、統計評価用情報Aが示す統計的分布に従った故障リスク指標の推移をモデル化したものであり、このモデル式のパラメータ値が第1の推定値となる。
“統計的分布パラメータ”の項目には、第1の推定値の統計的推定で用いられた統計的分布を規定するパラメータが設定される。統計的分布がワイブル分布に従う場合、ワイブル分布を規定する累積密度関数の形状パラメータγと尺度パラメータφが設定される。
“時間スケール係数”は、上記モデル式において、故障リスクが増加する速度に関するパラメータである。“リスク重み係数”は、上記モデル式において、故障リスクの重みの度合いに関するパラメータである。“安全マージン”は、上記モデル式で推定された作業間隔での保守作業日時から遡る時間間隔を示すパラメータである。このパラメータにより部品の保守作業が前倒しで実行される。
第1の記憶部5は、データベース100がある記憶装置に設けてもよいが、図4Aに示す処理回路104の内部メモリに設けてもよい。また、第1の記憶部5は、図4Bに示すメモリ105に設けてもよい。
すなわち、故障リスク指標推定装置1は、FMEA結果DB2から読み出したFMEA結果と統計評価用情報Aとに基づいて、故障リスク指標の推移を示すモデル式を構築し、FMEA結果DB2と既定作業間隔DB3とから読み出した情報に基づいて、モデル式から算出した保守作業間隔と既定された保守作業間隔との差分が最も小さくなるモデル式のパラメータ値を統計的に推定するための処理回路を備える。
処理回路は、専用のハードウェアであっても、メモリに記憶されたプログラムを実行するCPU(Central Processing Unit)であってもよい。
モデル式構築部4aおよびパラメータ推定部4bのそれぞれの機能を別々の処理回路で実現してもよいし、これらの機能をまとめて1つの処理回路で実現してもよい。
ソフトウェアまたはファームウェアはプログラムとして記述され、メモリ105に記憶される。プロセッサ106は、メモリ105に記憶されたプログラムを読み出して実行することにより各部の機能を実現する。
すなわち、故障リスク指標推定装置1は、プロセッサ106により実行されるときに、図5に示すステップST1、ステップST2が結果的に実行されるプログラムを記憶するためのメモリ105を備える。また、これらのプログラムは、モデル式構築部4aおよびパラメータ推定部4bの手順または方法をコンピュータに実行させるものである。
このように、処理回路は、ハードウェア、ソフトウェア、ファームウェアまたはこれらの組み合わせによって上記機能のそれぞれを実現することができる。
図5は故障リスク指標推定装置1の動作を示すフローチャートであり、故障リスク指標の推定値を求めて第1の記憶部5に記憶するまでの一連の処理を示している。図6は、故障リスク指標R(t)と中間評価指標S,W,Mとの関係を示す図であり、保守作業後の経過時間における故障リスク指標R(t)の推移を示している。図7は、FMEA結果の評価項目C,E,Dと中間評価指標S,W,Mとの関連性を示す図である。
以下、故障リスク指標推定装置1の動作を、図6と図7を参照しながら、図5に沿って説明する。
故障リスク指標R(t)は、下記式(1)に示すように、統計的分布f(t)に従って推移(時間変化)する。下記式(1)において、時間スケール係数Sは、R(t)の時間方向の変化を調整するための係数であり、故障リスクが増加する速度に関するパラメータである。リスク重み係数Wは、R(t)の大きさを調整するための係数であり、故障リスクの重みの度合いに関するパラメータである。
R(t)=W×f(S×t) ・・・(1)
T=TE-M ・・・(2)
また、上記式(1)は、形状パラメータγと尺度パラメータφを用いて、下記式(4)で表すことができる。下記式(4)において、尺度パラメータφは、時間スケール係数Sの積としてのみ登場するので、φ=1としてSのみを推定すべきパラメータとしている。
f(t)=1-exp(-(t/φ)γ) ・・・(3)
R(t)=W×(1-exp(-(S×t)γ)) ・・・(4)
TE=(1/S)×(-ln(1-(1/W)))1/γ ・・・(5)
故障の頻度レベルCは、評価対象の部品に故障が発生する頻度の指標になり得るので、故障リスクの増加速度に関する時間スケール係数Sと関連性がある。
影響の大きさレベルEは、評価対象の部品に故障が与える影響の大きさの指標になり得るので、故障リスクの重みの度合いに関するリスク重み係数Wと、保守作業を前倒しする度合いに関する安全マージンMとの両方に関連性がある。
検出可能性レベルDは、評価対象の部品に発生した故障の検出しやすさの指標になり得るので、保守作業を前倒しする度合いに関する安全マージンMに関連性がある。
図8は、モデル式構築部4aおよびパラメータ推定部4bの動作を示すフローチャートである。以下では、統計的分布がワイブル分布であるものとする。
モデル式構築部4aが、FMEA結果DB2と既定作業間隔DB3の各テーブルデータを併合する(ステップST1a)。テーブルデータは、図2Aおよび図2Bに示したような項目ごとの情報から構成されるデータであり、テーブルデータのうち、部品ID、点検項目IDおよびこれに続く横並びの情報がレコードデータである。
モデル式構築部4aは、既定作業間隔DB3からテーブルデータを読み出し、このテーブルデータにおける部品IDおよび点検項目IDに基づいて同じ部品IDおよび点検項目IDを有するFMEA結果DB2のテーブルデータを検索する。
モデル式構築部4aは、この検索により特定されたテーブルデータのFMEA結果を、既定作業間隔DB3から読み出したテーブルデータに組み合わせる、いわゆる併合を実行する。
FMEA結果DB2のテーブルデータのうち、既定作業間隔DB3のテーブルデータと同じ部品IDおよび点検項目IDを有さないテーブルデータについても、図9に示す組み合わせ情報にそのまま追加される。
モデル式構築部4aは、上記組み合わせ情報のFMEA結果のうち、FMEAの故障に関する評価項目とこの評価項目での評価レベルとが同一であるものに対して、共通の中間評価指標のパラメータを割り当ててモデル式を構築する。
共通のパラメータには、部品IDおよび点検項目IDが異なるデータ間の上記モデル式においても同じ値が設定される。これにより、異なる部品または点検項目で共通して作用する故障の影響を評価することができる。
上記の組み合わせ情報のFMEA結果のうち、影響の大きさレベルEの評価レベルが“3”であるものの一つに、(部品ID,点検項目ID)=(EQ001,MT002)がある。モデル式構築部4aは、図10に示すように、影響の大きさレベルEに関連性のあるリスク重み係数Wのパラメータに対して共通のパラメータW3を割り当てる。
(EQ001,MT001)のデータと(EQ001,MT002)のデータとでは、FMEA結果の評価項目である検出可能性レベルDも評価レベルがともに“3”である。このため、モデル式構築部4aは、図10に示すように、検出可能性レベルDに関連性のある安全マージンMのパラメータに対して共通のパラメータM3,3を割り当てる。
(EQ001,MT001)のデータについての中間評価指標のパラメータは、S2、W3、M3,3であるので、モデル式は下記のようになる。
T=TE-M3,3
R(t)=W3×{1-exp(-(S2×t)γ)}
TE=(1/S2)×(-ln(1-(1/W3)))1/γ
統計的分布f(t)がワイブル分布に従うので、作業間隔Tは、図11に示すように、中間評価指標のパラメータによって決定される変数の形で求められる。図11において、Ziは、下記式(6)で表されるパラメータである。
Zi=(-ln(1-(1/Wi)))1/γ ・・・(6)
これらのパラメータの推定方法としては、例えば、共役方向法が挙げられる。ただし、既定された作業間隔TSとの誤差が最小となるパラメータを推定することができる方法であれば、共役方向法に限定されるものではない。
このようにすることで、統計的推定に用いた情報を再利用することができ、推定に要する処理負荷を軽減することができる。
図12は、この発明の実施の形態2に係る故障リスク指標推定装置1Aの機能構成を示すブロック図である。図12において、図1と同一構成要素には同一符号を付して説明を省略する。故障リスク指標推定装置1Aは、実施の形態1で示した構成に加え、設備情報DB6、保守実績DB7、故障実績DB8、絞り込み部9、絞り込みデータ記憶部10、指標推定部11、第2の記憶部12および併合部13を備える。
“保守開始日時”の項目には、部品の点検項目ごとの保守契約が開始された日時が設定される。なお、保守契約中に、個別に行われる保守作業の実施日時が、図13Bを用いて後述する保守作業実施日時である。
“設備ID”、“部品ID”および“点検項目ID”については、図13Aと図2Aとに示したものと同じである。“保守作業実施日時”の項目には部品ごとの保守作業の実施日時が設定される。
例えば、絞り込み部9は、保守実績データごとに保守作業実施後の無故障の間隔を集計し、集計した無故障の間隔を示す情報をFMEA結果ごとに分類して絞り込みデータ記憶部10に記憶する。
図14Aにおいて、“故障の頻度レベル”と“影響の大きさレベル”と“検出可能性レベル”は、FMEAの評価項目であり、図2Aに示したものと同じである。
“部品ID”および“点検項目ID”は、図2Aに示したものと同じである。“保守作業実施日時”は、図13Bに示したものと同じである。
“無故障継続月数”の項目には、保守作業後に無故障で次の保守作業に至った月数、保守作業後に故障が発生するまでの月数および保守作業後に無故障で現在に至るまでの月数のうちのいずれかが設定される。“故障発生フラグ”の項目には、部品に故障が発生したか否かを示す値が設定される。例えば、部品に故障が発生すると、“1”が設定され、故障が発生していなければ、“0”が設定される。
例えば、指標推定部11は、絞り込みデータ記憶部10から処理対象のFMEA結果に対応する情報を読み出し、読み出した情報に基づいて部品ごとおよび点検項目ごとに故障リスク指標を統計的に推定する。この推定によって得られた故障リスク指標の推定値は、推定に用いられた実データ数とともに第2の記憶部12に記憶される。
例えば、併合部13は、第1の推定値の推定で想定したデータ数と第2の推定値の推定に用いた実データ数とに応じて第1の推定値と第2の推定値とを按分して、最終的な故障リスク指標の推定値を算出する。故障リスク指標の推定値を示す情報Bは併合部13から出力される。
設備情報DB6、保守実績DB7および故障実績DB8のそれぞれに記憶された情報は、DB入出力インタフェース101を通して絞り込み部9に入力される。
第1の記憶部5、絞り込みデータ記憶部10および第2の記憶部12は、データベース100がある記憶装置に設けることが考えられるが、図4Aに示す処理回路104の内部メモリに設けてもよい。また、第1の記憶部5、絞り込みデータ記憶部10および第2の記憶部12は、図4Bに示すメモリ105に設けてもよい。
すなわち、故障リスク指標推定装置1Aは、前述した各部の機能での処理を行うための処理回路を備える。処理回路は、専用のハードウェアであっても、メモリに記憶されたプログラムを実行するCPUであってもよい。
指標推定部4、絞り込み部9、指標推定部11および併合部13のそれぞれの機能を別々の処理回路で実現してもよいし、これらの機能をまとめて1つの処理回路で実現してもよい。
ソフトウェアまたはファームウェアはプログラムとして記述され、メモリ105に記憶される。プロセッサ106は、メモリ105に記憶されたプログラムを読み出して実行することにより各部の機能を実現する。
すなわち、故障リスク指標推定装置1Aは、プロセッサ106によって実行されるときに、図15に示すステップST1bからステップST8bまでの処理が結果的に実行されるプログラムを記憶するためのメモリ105を備える。
また、これらのプログラムは、指標推定部4、絞り込み部9、指標推定部11、および併合部13の手順または方法をコンピュータに実行させるものである。
図15は故障リスク指標推定装置1Aの動作を示すフローチャートであり、故障リスク指標の第1の推定値と第2の推定値を求めて最終的な推定値を出力するまでの一連の処理を示している。
絞り込み部9は、設備情報DB6、保守実績DB7および故障実績DB8に記憶された情報を、FMEA結果ごとに対応する情報に分類する(ステップST1b)。
絞り込み部9により分類された情報は、絞り込みデータ記憶部10に記憶される。
指標推定部11は、上記推定で得られた第2の推定値と実データ数を、部品IDおよび点検項目IDに対応付けて第2の記憶部12に記憶する(ステップST3b)。
FMEA結果DB2に記憶されている全てのFMEA結果を処理した場合(ステップST4b;YES)、ステップST5bの処理に移行する。
指標推定部4は、推定した第1の推定値を、部品IDおよび点検項目IDに対応付けて第1の記憶部5に記憶する(ステップST6b)。
例えば、指標推定部4による第1の推定値の推定を、指標推定部11による第2の推定値の推定よりも先に行ってもよい。また、指標推定部4による第1の推定値の推定と指標推定部11による第2の推定値の推定とを並行して行ってもよい。
図16は、絞り込み部9の動作を示すフローチャートであって、設備情報DB6、保守実績DB7および故障実績DB8に記憶された情報を分類して絞り込みデータ記憶部10に記憶するまでの一連の処理を示している。
まず、絞り込み部9は、FMEA結果DB2、設備情報DB6および保守実績DB7の各テーブルデータを併合する(ステップST1c)。
なお、絞り込み部9は、設備情報と同じ設備ID、部品IDおよび点検項目IDの保守実績データがない場合、検索に用いた設備ID、部品IDおよび点検項目IDのレコードデータを削除せずに併合後のテーブルデータに残す。
絞り込み部9は、設備情報DB6における全てのテーブルデータに上記処理を実行することで、設備情報と保守実績データとがFMEA結果ごとに分類された情報を生成する。
絞り込み部9は、上記併合したテーブルデータのレコードデータに対して“無故障継続月数”および“故障発生フラグ”の項目を追加する。
絞り込み部9は、検索結果の故障実績データに基づいて、部品に故障が発生しなかった無故障継続月数を算出して、算出した月数を“無故障継続月数”の項目に設定し、故障が発生したことを示す値である“1”を“故障発生フラグ”の項目に設定する。
なお、次に保守作業の日時が決まっていない場合は、現時点までの間隔が“無故障継続月数”の項目に設定される。
絞り込み部9は、ステップST1cで併合したテーブルデータにおける全てのレコードデータに対して上記処理を実行する。これにより、設備情報と保守実績データと故障実績データとが、FMEA結果ごとに分類された情報が生成される。
図17は、指標推定部11の動作を示すフローチャートであり、第2の推定値を推定してから第2の記憶部12に記憶するまでの一連の処理を示している。
ステップST1dにおいて、指標推定部11は、絞り込みデータ記憶部10に記憶されたテーブルデータから、処理対象の部品IDおよび点検項目IDが同一のレコードデータを検索する。指標推定部11は、検索したレコードデータにおいて“無故障継続月数”の項目に設定された値が、保守作業後の経過時間t(t=1,2,・・・)以下のレコードデータの数を算出する。このように算出されたレコードデータの数が、保守作業後の経過月数に応じた部品の延べ台数となる。
指標推定部11は、検索したレコードデータに基づいて、部品IDごとおよび点検項目IDごとの、保守作業後の経過月数に応じた故障件数を算出する(ステップST2d)。
次に、指標推定部11は、上記保守作業後の経過月数に応じた故障件数を上記延べ台数で割った値である実績故障率を算出する(ステップST3d)。
リスク重み係数Wの値は、FMEA結果ごとに設定される。例えば、ユーザから受け付けた値を設定してもよく、実績データから求めた故障発生時の損失額×故障率との差分が最も小さくなる係数の値を設定してもよい。安全マージンMの値は、ユーザから受け付けた値を設定してもよく、M=0としてもよい。
なお、統計的分布としてワイブル分布を使用したが、ガンマ分布、対数正規分布などの統計的分布であってもよく、実績データとの誤差が最小となる統計的分布であればよい。
絞り込みデータ記憶部10に記憶されているテーブルデータの部品IDおよび点検項目IDの組み合わせのうち、未処理の組み合わせがある場合(ステップST5d;NO)、ステップST1dに戻り、前述した処理を繰り返す。
部品IDおよび点検項目IDの全ての組み合わせを処理した場合(ステップST5d;YES)、指標推定部11は、推定したパラメータ値である第2の推定値を、部品IDおよび点検項目IDに対応付けて第2の記憶部12に記憶する(ステップST6d)。
統計的分布がワイブル分布に従うことから、図14Bに示した“統計的分布”の項目に“ワイブル分布”が設定され、“統計的分布パラメータ”の項目には、形状パラメータγの値が設定される。
図18は、併合部13の動作を示すフローチャートであり、第1の推定値と第2の推定値とから最終的な故障リスク指標の推定値を算出して出力するまでの一連の処理を示している。併合部13は、処理対象の部品IDおよび点検項目ID、これらに対応する第2の推定値および実データ数を第2の記憶部12から読み出す(ステップST1e)。
第1の記憶部5に記憶されている第1の推定値のうち、第2の記憶部12から読み出した第2の推定値に対応する部品IDおよび点検項目IDと同じ部品IDおよび点検項目IDを有さないレコードデータについては、第1の記憶部5からそのまま読み出される。
例えば、併合部13は、統計評価用情報Aにおける許容誤差Δおよび信頼率αを使用して、下記式(7)からNIを算出する。下記式(7)において、z(α)は、標準正規分布の上側100α%を表す。Δ=0.1、α=0.99(99%)であれば、z(α)=2.326であり、NI=2168となる。
NI=(((4/Δ2)+(1/2))×z(α)2) ・・・(7)
併合部13は、保守作業後の経過時間ごとの部品の延べ台数をNA+NIとし、故障件数をNA×RA(t)+NI×RI(t)として故障リスク指標RF(t)を改めて統計的に推定する。推定方法には、例えば、共役方向法が用いられる。ここまでの処理が按分処理である。このように、併合部13が、第1の推定値の統計的推定で想定されたデータ数であるNIを第2の推定値との按分に用いる。これにより、第1の推定値と第2の推定値とを適切に按分することができる。
第1の記憶部5および第2の記憶部12のそれぞれに記憶された情報のうち、按分していない部品IDおよび点検項目IDの組み合わせがあれば(ステップST5e;NO)、未処理の組み合わせに対してステップST1eからの処理を繰り返す。
全ての部品IDおよび点検項目IDの組み合わせを按分した場合(ステップST5e;YES)、併合部13は、故障リスク指標RF(t)のパラメータ値から、最終的な故障リスク指標の推定値を示す情報Bを生成して出力する(ステップST6e)。
Claims (7)
- 設備を構成する部品の点検項目ごとの故障モード影響解析結果を示す情報および故障リスク指標の推定に用いる統計的分布を示す情報に基づいて、前記統計的分布に従った前記故障リスク指標の推移を示すモデル式を構築するモデル式構築部と、
設備を構成する部品の点検項目ごとの故障モード影響解析結果を示す情報および部品の点検項目ごとに既定された保守作業間隔を示す情報に基づいて、前記モデル式から算出した保守作業間隔と前記既定された保守作業間隔との差分が最も小さくなる前記モデル式のパラメータ値を統計的に推定するパラメータ推定部と
を備えたことを特徴とする故障リスク指標推定装置。 - 設備、設備を構成する部品および部品ごとの点検項目を含む設備情報、設備を構成する部品の点検項目ごとの保守作業の実績データおよび設備を構成する部品ごとの故障の実績データを、故障モード影響解析結果ごとに対応する情報に分類する絞り込み部と、
前記絞り込み部により分類された情報に基づいて、前記保守作業の実績データと前記故障の実績データとの関係に応じた前記故障リスク指標の推定値を推定する指標推定部と、
前記パラメータ推定部により推定されたパラメータ値である第1の推定値と前記指標推定部により推定された推定値である第2の推定値とを按分して、最終的な前記故障リスク指標の推定値を算出する併合部と
を備えたことを特徴とする請求項1記載の故障リスク指標推定装置。 - 前記併合部は、前記第1の推定値の統計的推定で想定されたデータ数を前記第2の推定値との按分に用いること
を特徴とする請求項2記載の故障リスク指標推定装置。 - 前記モデル式構築部は、故障モード影響解析の故障に関する評価項目と当該評価項目での評価レベルとが同一である場合、前記評価項目に関連する前記モデル式のパラメータに共通のパラメータを割り当てること
を特徴とする請求項1記載の故障リスク指標推定装置。 - 前記パラメータ推定部は、互いに類似する点検項目間の一方についてのパラメータ値の統計的推定で用いた情報を、点検項目間の他方についてのパラメータ値の統計的推定に用いること
を特徴とする請求項1記載の故障リスク指標推定装置。 - モデル式構築部が、設備を構成する部品の点検項目ごとの故障モード影響解析結果を示す情報および故障リスク指標の推定に用いる統計的分布を示す情報に基づいて、前記統計的分布に従った前記故障リスク指標の推移を示すモデル式を構築するステップと、
パラメータ推定部が、設備を構成する部品の点検項目ごとの故障モード影響解析結果を示す情報および部品の点検項目ごとに既定された保守作業間隔を示す情報に基づいて、前記モデル式から算出した保守作業間隔と前記既定された保守作業間隔との差分が最も小さくなる前記モデル式のパラメータ値を統計的に推定するステップと
を備えたことを特徴とする故障リスク指標推定方法。 - 絞り込み部が、設備、設備を構成する部品および部品ごとの点検項目を含む設備情報、設備を構成する部品の点検項目ごとの保守作業の実績データおよび設備を構成する部品ごとの故障の実績データを、故障モード影響解析結果ごとに対応する情報に分類するステップと、
指標推定部が、前記絞り込み部により分類された情報に基づいて、前記保守作業の実績データと前記故障の実績データとの関係に応じた前記故障リスク指標の推定値を推定するステップと、
併合部が、前記パラメータ推定部により推定されたパラメータ値である第1の推定値と前記指標推定部により推定された推定値である第2の推定値とを按分して、最終的な前記故障リスク指標の推定値を算出するステップと
を備えたことを特徴とする請求項6記載の故障リスク指標推定方法。
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