JP7389727B2 - Fault diagnosis device, fault diagnosis system, and fault diagnosis program - Google Patents

Description

The present invention relates to a fault diagnosis device, a fault diagnosis system, and a fault diagnosis program, and more particularly to a fault diagnosis device, a fault diagnosis system, and a fault diagnosis program for diagnosing a fault in a fuel cell cogeneration system.

When a conventional fuel cell system control device detects an abnormality in the operation of the fuel cell system, it identifies abnormality information including the abnormality location and abnormality cause, and limits the number of times set for each identified abnormality information to a first number of times. , a retry operation is executed, and if an abnormality is detected after the retry operation exceeds the first number of times, a first abnormality process is executed, which is executed in parallel with the execution of the first abnormality process, and the abnormality is detected. Information is classified as a batch according to classification requirements under predetermined conditions, and if an abnormality is detected after the retry operation exceeds the second number of times set for each classification, the second abnormality is detected. The process is executed (for example, see Patent Document 1).

In addition, conventional failure location diagnosis systems use a logistic regression model to determine whether a given location out of multiple locations in the power generation system is failing and other locations are failing based on multiple explanatory variables representing operating information of the power generation system. In this method, an objective variable representing the probability that the object is not detected is specified (see, for example, Patent Document 2).

Japanese Patent Application Publication No. 2018-169903 Japanese Patent Application Publication No. 2020-030757

According to the technology described in Patent Document 1, convenience is ensured by the first abnormality processing, and safety is ensured by the second abnormality processing. However, since there is not necessarily a one-to-one correspondence between abnormality information and faulty parts, failure diagnosis work may be performed on site to identify faulty parts, which places a heavy burden on the workers. was on.

Further, according to the technology described in Patent Document 2, failure diagnosis is performed using a logistic regression model, so if a highly accurate logistic regression model can be used, there is no need to perform failure diagnosis work on site. There are cases. However, since the error is immediately determined without performing a retry operation when an error is detected, the diagnostic accuracy cannot be said to be sufficient.

The present invention has been made in view of the above-mentioned circumstances, and provides a failure diagnosis device and a failure diagnosis device capable of accurately diagnosing a fuel cell cogeneration system while reducing the on-site workload of a worker. The purpose is to provide a diagnostic system and a fault diagnosis program.

In order to achieve the above object, a failure diagnosis device according to one aspect of the present invention performs a retry operation of the fuel cell cogeneration system when the fuel cell cogeneration system detects an abnormality in operation, and performs the retry operation. As a result, when a plurality of abnormalities are detected, a plurality of operation history data regarding the operation history of the fuel cell cogeneration system corresponding to each of the plurality of abnormalities is obtained from the fuel cell cogeneration system. A function of the fuel cell cogeneration system is generated by performing multivariate analysis or machine learning on an acquisition unit and a group of operation history data obtained in the past in association with functional units of the fuel cell cogeneration system. A plurality of operation history data acquired by the acquisition unit or a plurality of data obtained by processing the plurality of operation history data are separately used for a failure diagnosis model for each functional unit that determines the presence or absence of a failure for each unit. and an estimating unit that estimates the presence or absence of a failure for each functional unit of the fuel cell cogeneration system, using a plurality of determination results input to the failure diagnosis model and separately output from the failure diagnosis model.

Further, a fault diagnosis system according to one aspect of the present invention includes a fuel cell cogeneration system and a fault diagnosis device connected to the fuel cell cogeneration system, and the fuel cell cogeneration system When an abnormality in operation is detected, a retry operation is performed on the own system, and if multiple abnormalities are detected as a result of the retry operation, the fault diagnosis device is configured to respond to each of the multiple abnormalities. transmitting a plurality of operation history data regarding the operation history of its own system, and the failure diagnosis device includes an acquisition unit that acquires the plurality of operation history data transmitted from the fuel cell cogeneration system; is generated by performing multivariate analysis or machine learning on a group of operation history data associated with functional units of the fuel cell cogeneration system, and determines whether or not there is a failure for each functional unit of the fuel cell cogeneration system. A plurality of operation history data acquired by the acquisition unit or a plurality of data obtained by processing the plurality of operation history data are separately inputted into the failure diagnosis model for each functional unit to be processed, and the failure diagnosis model The fuel cell cogeneration system includes an estimation section that estimates the presence or absence of a failure for each functional section of the fuel cell cogeneration system using a plurality of determination results separately output from the fuel cell cogeneration system.

Further, a fault diagnosis program according to one aspect of the present invention causes a computer to function as each part included in the above-mentioned fault diagnosis apparatus.

As described in detail above, according to the present invention, failure diagnosis of a fuel cell cogeneration system can be performed with high accuracy while reducing the on-site burden on the person in charge of the work.

1 is a diagram showing an example of a configuration of a failure diagnosis system according to a first embodiment. FIG. 2 is a block diagram showing an example of the configuration of a CGS according to the first embodiment. 3 is a flowchart illustrating an example of the flow of processing by a control program according to the first embodiment. 1 is a block diagram showing an example of an electrical configuration of a failure diagnosis device according to a first embodiment. FIG. 1 is a block diagram showing an example of a functional configuration of a failure diagnosis device according to a first embodiment; FIG. It is a figure showing an example of the 1st data table concerning an embodiment. It is a figure showing an example of the 2nd data table concerning an embodiment. It is a figure provided for explanation of a precision rate concerning an embodiment. 3 is a flowchart illustrating an example of the flow of processing by the failure diagnosis program according to the first embodiment. 7 is a flowchart illustrating an example of the flow of learning processing by the failure diagnosis program according to the first embodiment. FIG. 2 is a block diagram illustrating an example of a functional configuration of a failure diagnosis device according to a second embodiment. It is a figure which shows an example of the 3rd data table based on embodiment. It is a figure which shows an example of the 4th data table based on embodiment. 7 is a flowchart illustrating an example of the flow of processing by a failure diagnosis program according to the second embodiment. 12 is a flowchart illustrating an example of the process flow of route diagnosis A according to the second embodiment. 12 is a flowchart illustrating an example of the process flow of route diagnosis B according to the second embodiment. It is a flow chart which shows an example of the flow of processing of route diagnosis C concerning a 2nd embodiment. It is a flow chart which shows an example of the flow of processing of route diagnosis E concerning a 2nd embodiment.

EMBODIMENT OF THE INVENTION Hereinafter, with reference to drawings, an example of the form for implementing this invention is demonstrated in detail.

[First embodiment]
FIG. 1 is a diagram showing an example of the configuration of a failure diagnosis system 100 according to the first embodiment.

As shown in FIG. 1, a failure diagnosis system 100 according to the present embodiment includes a fuel cell cogeneration system (hereinafter simply referred to as "CGS") 40, a failure diagnosis device 10, and a maintenance terminal 70. ing. The failure diagnosis system 100 is configured as a system for diagnosing a failure of the CGS 40.

The CGS 40 is, for example, ENE-FARM (registered trademark), and is installed at the user's home 30. The failure diagnosis device 10 is a device for diagnosing a failure of the CGS 40, and is connected to the CGS 40 via a network N. For example, a general-purpose computer device such as a server computer or a personal computer (PC) is applied to the failure diagnosis device 10. For example, a network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet is applied to the network N.

The maintenance terminal 70 is a terminal owned by a worker 60 who performs maintenance work on the CGS 40. The maintenance terminal 70 is connected to the failure diagnosis device 10 via the network N. The maintenance terminal 70 may be, for example, a portable tablet terminal, a smartphone, a notebook PC, or the like.

When an abnormality in operation (hereinafter referred to as an "error") is detected multiple times due to the retry operation of the CGS 40, the failure diagnosis device 10 according to the present embodiment collects operation history data for each of the multiple error detections. are input into the fault diagnosis model separately to perform fault diagnosis.

Specifically, in FIG. 1, the CGS 40 issues an error message after confirming the error through a retry operation. At this time, the operation history data of the own system is automatically transmitted to the failure diagnosis device 10. The operation history data is, for example, data related to at least one operation history at the time of error detection, several seconds before error detection (for example, 10 seconds or less), and several seconds after error detection (for example, 10 seconds or less). The operation history data includes, for example, detection values of various sensors, data representing the operating state of auxiliary equipment (eg, valves, pumps, etc.), error codes representing the type of error, and the like.

The failure diagnosis device 10 stores a failure diagnosis model in advance in a storage unit. The failure diagnosis model is a mathematical model for diagnosing a failure for each functional unit of the CGS 40. A functional part includes at least one component (eg, a pump, a valve, a pipe, a tank, etc.). The failure diagnosis model of the CGS 40 is a model generated by performing multivariate analysis or machine learning on a group of operation history data obtained in the past in association with functional parts. Multivariate analysis includes, for example, logistic regression. Examples of machine learning include random forests, neural networks, support vector machines, and the like. The fault diagnosis model may be a two-class classification that determines the presence or absence of a fault for each functional unit, or may be a multi-class classification. The fault diagnosis device 10 processes the operation history data acquired from the CGS 40 as is or into data that can be input into a fault diagnosis model, and inputs the processed data into the fault diagnosis model. The fault diagnosis model outputs "faulty (faulty)" or "no faulty (healthy)" for each functional unit. Specifically, when the index value indicated by the data is greater than or equal to the threshold value, "failure present" is output, and when the index value indicated by the data is less than the threshold value, "no failure" is outputted. The failure diagnosis device 10 holds diagnosis results of failure diagnosis for each functional unit.

The worker (maintenance worker) 60 accesses the failure diagnosis device 10 from the maintenance terminal 70 and confirms the failure location indicated by the diagnosis result. Then, the worker 60 performs parts replacement work and the like at the site based on the diagnosis results obtained from the failure diagnosis device 10.

FIG. 2 is a block diagram showing an example of the configuration of the CGS 40 according to the first embodiment.

As shown in FIG. 2, the CGS 40 according to this embodiment is broadly divided into a control device 41 and a CGS main body 49. The control device 41 may be configured integrally with the CGS main body 49 or may be configured separately from the CGS main body 49.

The control device 41 functions as a controller for the CGS 40. The control device 41 includes a CPU (Central Processing Unit) 42, a ROM (Read Only Memory) 43, a RAM (Random Access Memory) 44, an input/output interface (I/O) 45, a storage section 46, and a communication section. 47, and a device interface (hereinafter referred to as "device I/F") 48.

The CPU 42, ROM 43, RAM 44, and I/O 45 are each connected via a bus. Each functional unit including a storage unit 46, a communication unit 47, and a device I/F 48 is connected to the I/O 45. Each of these functional units can communicate with the CPU 42 via the I/O 45.

As the storage unit 46, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like is used. The storage unit 46 stores a control program 46A for controlling the operation of the CGS main unit 49. Note that this control program 46A may be stored in the ROM 43.

The control program 46A may be installed in the control device 41 in advance, for example. The control program 46A may be realized by being stored in a nonvolatile storage medium, or distributed via the network N, and installed in the control device 41 as appropriate. Note that examples of nonvolatile storage media include CD-ROM (Compact Disc Read Only Memory), magneto-optical disk, HDD, DVD-ROM (Digital Versatile Disc Read Only Memory), flash memory, memory card, etc. Ru.

The communication unit 47 is connected to a network N such as the Internet, LAN, WAN, etc., and is capable of communicating with the external failure diagnosis device 10 via the network N.

A CGS main body section 49 is connected to the device I/F 48. That is, various electrical components included in the CGS main body section 49 are communicably connected to the CPU 42 via the device I/F 48.

The CGS main body portion 49 has a known configuration. Specifically, the CGS main body section 49 includes a fuel cell unit 50. The fuel cell unit 50 is provided with a fuel cell module 51, a gas path 52 connected to the fuel cell module 51, a reformed water path 53, and an air path 54. The CGS main body section 49 includes a plurality of functional sections, as described above. This functional unit includes at least one component. This functional section may be, for example, a plurality of parts forming the gas path 52, a plurality of parts forming the reformed water path 53, or a plurality of parts forming the air path 54. This functional section may be an individual component constituting the CGS main body section 49. These parts include, for example, pumps, valves, piping, tanks, and the like.

The fuel cell module 51 includes a fuel cell stack 51A and a reformer 51B. The reformer 51B reforms gas (for example, city gas) containing a hydrocarbon raw material. In the fuel cell stack 51A, a plurality of fuel cells each having an electrolyte layer, a fuel electrode, and an air electrode are stacked. The fuel cell stack 51A is configured to generate electricity and water by causing hydrogen in the reformed gas reformed from city gas by the reformer 51B to react with oxygen in the air. The fuel cell unit 50 includes auxiliary equipment such as valves and pumps for adjusting the supply amount of reformed gas and air, and a heat transfer medium (for example, water) to transfer the heat generated by power generation by the fuel cell stack 51A. and a heat exchanger for exchanging heat between the two.

The CGS main body 49 includes a hot water storage tank. In the CGS main body section 49, when water is supplied to the hot water storage tank, the water is supplied from the hot water storage tank to the heat exchanger of the fuel cell unit 50, and the water is heated in this heat exchanger with the heat of the fuel cell stack 51A. When water is heated in a heat exchanger, it turns into hot water. This hot water is supplied to a hot water storage tank and stored in this hot water storage tank. The hot water storage tank includes auxiliary equipment such as a valve and a pump for transferring water and hot water to and from the fuel cell unit 50, and a valve and a pump for discharging hot water stored in the hot water storage tank.

Further, the gas route 52 is a route through which city gas flows. The gas path 52 includes auxiliary equipment such as valves and pumps for adjusting the amount of city gas supplied. The reformed water path 53 is a path through which reformed water flows. The reformed water path 53 includes auxiliary equipment such as valves and pumps for adjusting the amount of reformed water supplied. The air path 54 is a path through which air flows. The air path 54 includes auxiliary equipment such as valves and pumps for adjusting the amount of air supplied.

The control device 41 is a controller that controls auxiliary equipment provided in the CGS main body 49. The control device 41 outputs control signals for controlling the auxiliary equipment to the CGS main unit 49, respectively. Further, the CGS main body 49 outputs operational data representing the operating status of the CGS main body 49 to the control device 41. This operation data includes, as feature quantities, data representing the operating states of auxiliary equipment (for example, voltage values, current values, etc.), detection values of various sensors provided in the CGS main body 49, and the like. Specifically, the data representing the operating state of the auxiliary equipment is, for example, in the case of a pump, data representing the operating amount of the pump, and in the case of a valve, data representing the opening/closing state of the valve. Further, the detected values of various sensors are, for example, values representing the respective flow rates of city gas, reformed water, and air in the case of piping, and values representing the water level in the case of tanks.

The control device 41 stores the time-series operating data acquired from the CGS main unit 49 in the storage unit 46 . The control device 41 has a function of detecting errors in the CGS main body 49 based on operational data acquired from the CGS main body 49 . Note that error detection is performed, for example, by comparing a feature amount obtained from driving data with a corresponding threshold value. Further, the control device 41 has a retry operation function. This retry operation function is a function that restarts the CGS 40 when an error is detected and detects the presence or absence of an error again. Note that, without restarting the CGS 40, the presence or absence of an error may be detected again after the CGS 40 is placed in an idling state (a state in which no power is generated) for a certain period of time. The control device 41 determines an error when a plurality of errors are detected by the retry operation function. The number of retry operations is predetermined, for example, according to an error code representing the type of error.

Here, with reference to FIG. 3, error notification processing by the control device 41 according to the present embodiment will be specifically described. Note that this error reporting process is executed by a control program 46A included in the control device 41.

FIG. 3 is a flowchart showing an example of the flow of processing by the control program 46A according to the first embodiment. In the example of FIG. 3, the number of abnormal value detections until an error is confirmed is two times (one retry operation), but the number of abnormal value detections may be three or more times (two or more retry operations).

First, the control program 46A stored in the storage unit 46 is activated by the CPU 42, and each step shown below is executed.

In step S101 of FIG. 3, the CPU 42 stores the operational data acquired in time series from the CGS main unit 49 in the storage unit 46, and determines whether an abnormal value indicating the occurrence of an error is detected based on the acquired operational data. Determine whether or not. If it is determined that an abnormal value has been detected (in the case of an affirmative determination), the process moves to step S102, and if it is determined that an abnormal value has not been detected (in the case of a negative determination), the process goes on standby in step S101.

In step S102, the CPU 42 selects, from among the time-series operation data stored in the storage unit 46, the time of error detection, several seconds before error detection (for example, 10 seconds or less), and several seconds after error detection (for example, 10 seconds or less). At least one operation data (seconds or less) is extracted as operation history data, and the extracted operation history data is held in the storage unit 46. As described above, this operation history data includes, for example, detection values of various sensors, data representing the operation status of auxiliary equipment, error codes, and the like. The operation history data held in the storage unit 46 is referred to as "data 1."

In step S103, the CPU 42 restarts the CGS 40 and performs a retry operation.

In step S104, the CPU 42 stores the operational data acquired in time series from the CGS main body 49 in the storage unit 46, and based on the acquired operational data, the CPU 42 stores the operational data within a predetermined time (for example, within 24 hours) after restarting the CGS 40. ) to determine whether an abnormal value has been detected again. If it is determined that the abnormal value has been re-detected (in the case of a positive determination), the process moves to step S105, and if it is determined that the abnormal value is not re-detected (in the case of a negative determination), the process returns to step S101 and the process is repeated.

In step S105, the CPU 42 selects, from among the time-series operation data stored in the storage unit 46, the time of error detection, several seconds before error detection (for example, 10 seconds or less), and several seconds after error detection (for example, 10 seconds or less). At least one operation data (seconds or less) is extracted as operation history data, and the extracted operation history data is held in the storage unit 46. The operation history data held in the storage unit 46 will be referred to as "data 2."

In step S106, the CPU 42 determines the error because it has detected multiple errors through the retry operation.

In step S107, the CPU 42 transmits the data 1 held in step S102 and the data 2 held in step S105 to the failure diagnosis device 10 via the communication unit 47, and ends the series of processing by the control program 46A. Note that one data file including data 1 and data 2 may be sent to the failure diagnosis device 10. In this case, the failure diagnosis device 10 may be configured to individually extract data 1 and data 2 from the data file.

FIG. 4 is a block diagram showing an example of the electrical configuration of the failure diagnosis device 10 according to the first embodiment.

As shown in FIG. 4, the failure diagnosis device 10 according to the present embodiment includes a CPU 11, a ROM 12, a RAM 13, an I/O 14, a storage section 15, a display section 16, an operation section 17, and a communication section 18. It is equipped with.

The CPU 11, ROM 12, RAM 13, and I/O 14 are each connected via a bus. Each functional section including a storage section 15, a display section 16, an operation section 17, and a communication section 18 is connected to the I/O 14. Each of these functional units can communicate with the CPU 11 via the I/O 14.

As the storage unit 15, for example, an HDD, SSD, flash memory, etc. are used. The storage unit 15 stores a failure diagnosis program 15A for diagnosing a failure of the CGS 40. Note that this failure diagnosis program 15A may be stored in the ROM 12.

The failure diagnosis program 15A may be installed in the failure diagnosis apparatus 10 in advance, for example. The fault diagnosis program 15A may be realized by being stored in a nonvolatile storage medium, or distributed via the network N, and installed in the fault diagnosis apparatus 10 as appropriate. Note that examples of nonvolatile storage media include CD-ROMs, magneto-optical disks, HDDs, DVD-ROMs, flash memories, and memory cards.

For example, a liquid crystal display (LCD), an organic EL (electro luminescence) display, or the like is used for the display unit 16. The display unit 16 may integrally include a touch panel. The operation unit 17 is provided with an operation input device such as a keyboard and a mouse. The display unit 16 and the operation unit 17 receive various instructions from the user of the failure diagnosis device 10. The display unit 16 displays various information such as the results of processing executed in response to instructions received from the user and notifications regarding the processing.

The communication unit 18 is connected to a network N such as the Internet, LAN, WAN, etc., and can communicate with the CGS 40 and the maintenance terminal 70 via the network N.

The CPU 11 of the failure diagnosis device 10 according to this embodiment functions as each unit shown in FIG. 5 by writing the failure diagnosis program 15A stored in the storage unit 15 into the RAM 13 and executing it.

FIG. 5 is a block diagram showing an example of the functional configuration of the failure diagnosis device 10 according to the first embodiment.

As shown in FIG. 5, the CPU 11 of the failure diagnosis device 10 according to this embodiment functions as an acquisition section 11A, an estimation section 11B, a learning section 11C, and an output section 11D.

Furthermore, as described above, the storage unit 15 stores in advance the failure diagnosis model 15B for each functional unit. In other words, one failure diagnosis model 15B corresponds to one functional unit (or component). As described above, the fault diagnosis model 15B is a mathematical model for diagnosing a fault for each functional unit of the CGS 40, which is obtained by multivariate analysis or machine learning. Furthermore, the storage unit 15 stores a first data table 15C and a second data table 15D.

The acquisition unit 11A acquires a plurality of pieces of operation history data transmitted from the CGS 40 described above. For example, data 1 and data 2 are acquired as a plurality of operation history data.

The estimation unit 11B processes the plurality of operation history data acquired by the acquisition unit 11A into a plurality of data that can be input into the failure diagnosis model 15B, and stores the plurality of data obtained by processing in the storage unit 15. The information is input separately to the failure diagnosis model 15B for each functional unit. Note that the operation history data may be input as is to the failure diagnosis model 15B without being processed. Then, the estimating unit 11B estimates the presence or absence of a failure for each functional unit of the CGS 40 using a plurality of determination results separately output from the failure diagnosis model 15B. Specifically, for example, the presence or absence of a failure is estimated using the first data table 15C or the second data table 15D.

The learning unit 11C generates the failure diagnosis model 15B by performing machine learning using a group of operation history data obtained in the past as learning data. The fault diagnosis model 15B generated by the learning unit 11C is stored in the storage unit 15. Note that the storage location of the failure diagnosis model 15B is not limited to the storage unit 15. For example, it may be stored in an external storage device. Further, in this embodiment, the configuration is such that the machine learning is performed by the own device, but the configuration may be such that the machine learning is performed by an external device. Furthermore, instead of machine learning, the fault diagnosis model 15B may be generated using multivariate analysis.

The output unit 11D outputs and holds the final diagnosis result for each functional unit estimated by the estimation unit 11B to, for example, the storage unit 15. Further, the output unit 11D transmits the final diagnosis result held in the storage unit 15 to the maintenance terminal 70 in response to the access from the maintenance terminal 70. Note that this final diagnosis result includes the diagnosis result (failure, soundness, suspected failure) and actions to be taken for each functional unit (or each part).

FIG. 6 is a diagram showing an example of the first data table 15C according to the present embodiment.

The first data table 15C shown in FIG. 6 is data showing the correspondence between the judgment pattern and the final diagnosis result when the number of abnormal value detections until the error is confirmed by the CGS 40 is 2 (the number of retries is 1). It's a table. In the example of FIG. 6, data 1 and data 2 are acquired as operation history data from the CGS 40, and data obtained by processing each of data 1 and data 2 is input into the failure diagnosis model 15B for each functional unit. The case is shown below.

Determination pattern 1 indicates a pattern in which the determination result of data 1 is "failure" and the determination result of data 2 is "failure". This determination pattern 1 indicates that the final diagnosis result (output) is "failure" and that the action that the worker 60 should take is "parts replacement." Further, determination pattern 2 indicates a pattern when the determination result of data 1 is "failure" and the determination result of data 2 is "sound". This determination pattern 2 indicates that the final diagnosis result (output) is "suspected failure" and that the response that the worker 60 should take is "confirm component operation." Further, determination pattern 3 indicates a pattern in which the determination result of data 1 is "healthy" and the determination result of data 2 is "failure". This determination pattern 3 indicates that the final diagnosis result (output) is "suspected failure" and that the response that the worker 60 should take is "confirm component operation." Furthermore, determination pattern 4 indicates a pattern in which the determination result of data 1 is "healthy" and the determination result of data 2 is "healthy." This determination pattern 4 indicates that the final diagnosis result (output) is "healthy" and the response that the worker 60 should take is "no".

That is, when the determination results for each of the plurality of data separately input to the failure diagnosis model 15B are failures, that is, determination pattern 1, the estimation unit 11B uses the first data table 15C in FIG. 6 as an example. , and the functional unit associated with the fault diagnosis model is presumed to be faulty. In addition, when the determination results for each of the plurality of data input separately to the failure diagnosis model 15B are all healthy, that is, determination pattern 4, the estimation unit 11B calculates, as an example, the first data table 15C in FIG. , and the functional unit associated with the failure diagnosis model 15B is presumed to be healthy. Furthermore, when one of the determination results for each of the plurality of data separately input to the failure diagnosis model 15B is failure and the other is healthy, that is, determination pattern 2 or determination pattern 3, the estimating unit 11B performs the following as an example: With reference to the first data table 15C in FIG. 6, the functional unit associated with the failure diagnosis model 15B is presumed to be a failure suspect.

FIG. 7 is a diagram showing an example of the second data table 15D according to the present embodiment.

The second data table 15D shown in FIG. 7 shows the relationship between the judgment pattern and the final diagnosis result when the number of abnormal value detections until the CGS 40 confirms the error is N times (N≧3, the number of retries is 2 or more). This is a data table showing correspondence relationships. In the example shown in FIG. 7, data 1, ..., data N are acquired as operation history data from the CGS 40, and the data obtained by processing each of data 1, ..., data N is processed for each functional unit. The case where the data is input to the fault diagnosis model 15B is shown.

In this case, the number of determination patterns is 2 (faulty or healthy) to the power of N, and failures and healthy patterns are mixed except for the two patterns in which all the patterns are failed or healthy. For this reason, the following three methods can be considered for determining the final diagnosis result (output): failure, suspected failure, and soundness.

(1) Based on the number of judgments of failure and soundness, which are the judgment results for each of a plurality of pieces of data input separately to the failure diagnosis model 15B, the functional unit associated with the failure diagnosis model 15B is determined to be faulty, healthy, or suspected of failure. Estimated as. In other words, the final diagnosis result (output) is the one with the greater number of judgments of failure and soundness (or the difference in the number of judgments is greater than the threshold), and the one with the same number of judgments of failure and soundness (or the difference between the number of judgments is the threshold) Failure is suspected in the following cases:

(2) Corresponds to the fault diagnosis model 15B based on the fault score derived from the judgment results for each of the plurality of data input separately to the fault diagnosis model 15B and the precision rate of the fault judgment of the fault diagnosis model 15B. The attached functional part is presumed to be faulty, healthy, or suspected to be faulty. That is, a failure score is obtained from the precision rate of failure diagnosis model 15B at the time of failure determination, and the final diagnosis result (output) is determined. For example, if the precision at failure determination is 70%, the failure score is calculated as follows. Note that the failure score may be expressed as a failure probability (expressed as a percentage).

Determined as a failure 0 out of N times → failure score = 0
Determined as a failure 1 out of N times → Failure score = 1-(1-0.7) = 0.7
Determined as failure 2 out of N times → failure score = 1-(1-0.7) ² =0.91

A threshold value (threshold value 1) for determining the final diagnosis result (output) as a failure and a threshold value (threshold value 2) for determining it as healthy are determined respectively, and if the failure score is equal to or higher than the threshold value 1, the failure occurs. If it is less than or equal to threshold 2, it is considered to be healthy, and if it is greater than threshold 2 and less than threshold 1 (intermediate value), it is considered to be malfunctioning. Note that although the above example uses the precision rate at the time of failure determination, the precision rate at the time of health determination or the recall rate of failure detection may be used for determination. In this case, a soundness score derived from the above-mentioned determination result and the precision rate at the time of health determination or the recall rate of failure detection is used.

(3) A fault score derived from the judgment results for each of the plurality of data input separately to the fault diagnosis model 15B and the precision rate at the time of fault judgment of the fault diagnosis model 15B, and A function is associated with the fault diagnosis model 15B based on the soundness score derived from the judgment result for each of the plurality of input data and the precision rate or recall rate of failure detection in the soundness judgment of the fault diagnosis model 15B. The part is presumed to be defective, sound, or suspected to be defective. That is, a score is calculated using both the precision rate at the time of failure determination and the precision rate at the time of health determination (or the recall rate of failure detection), and the final diagnosis result (output) is determined. For example, when N=3, the precision at failure determination is 90%, and the precision at health determination is 70%, the failure score and health score are determined as follows.

0 out of 3 times was determined to be a failure and 3 times were determined to be healthy → failure score = 0, health score = 1-(1-0.7) ³ = 0.973
1 out of 3 times was determined to be a failure and 2 times were determined to be healthy → failure score = 1-(1-0.9) = 0.9, health score = 1-(1-0.7) ² = 0.91
Two out of three times were determined to be faulty and one time was determined to be sound → failure score = 1-(1-0.9) ² =0.99, health score = 1-(1-0.7) = 0.7
3 out of 3 failures, 0 times determined to be healthy → failure score = 1-(1-0.9) ³ = 0.999, health score = 0

Here, it is assumed that the total score = failure score - soundness score. In this case, the final diagnosis result (output) is determined to be a failure when the overall score is a positive value, healthy when it is a negative value, and suspected failure when it is 0. Alternatively, a threshold value may be provided for determining a suspected failure, and when the total score is within the ±threshold value (for example, ±0.1), the final diagnosis result (output) may be determined as a suspected failure.

Next, with reference to FIG. 8, the above-mentioned precision rate will be specifically explained.

FIG. 8 is a diagram for explaining the precision rate according to this embodiment.

The table shown in FIG. 8 shows the correspondence between predictions and actual conditions regarding the failure diagnosis model for component A. The number inside the frame indicates the number of data. In other words, the number of data when the prediction is sound and the actual situation is sound is 28 (=A). The number of data when the prediction is failure and the actual condition is healthy is 1 (=B). The number of data when the prediction is healthy and the actual situation is a failure is 12 (=C). When the prediction is a failure and the actual condition is a failure, the number of data is 9 (=D).

In this case, the precision rate at the time of failure determination = D/(B+D) = 9/10 = 90%. That is, in this case, the precision rate is the ratio of the number of data items that are actually defective to the total number of data items that are predicted to be defective. Further, the accuracy rate at the time of soundness determination = A/(A+C) = 28/40 = 70%. In other words, in this case, the precision is the ratio of the number of data that is actually healthy to the total number of data that is predicted to be healthy.

Note that in the example of FIG. 8, there is more data that is determined to be healthy, so as the number of data increases, the conformity rate at the time of healthy determination tends to increase. Therefore, the determination method (3) described above may reduce the accuracy of the final diagnosis result (output). In normal cases, the number of times an abnormal value is detected before an error is determined is often small, so basically the above-mentioned determination method (2) is considered to be the most effective.

Furthermore, recall may be used instead of the precision described above. The recall rate represents the rate at which failures are found, and in the example of FIG. 8, it is expressed as recall rate=D/(C+D). In other words, the recall rate is the ratio of the number of data predicted to be a failure to the total number of data that actually are a failure. The higher this recall rate is, the sounder it is. Using this recall rate, the final diagnosis result (output) may be determined to be healthy when the risk of overlooking a failure is sufficiently small based on the determination results with N data. At this time, failure is calculated from the precision rate, and soundness is calculated from the recall rate. Note that if both the precision rate and the recall rate are high, it may be considered as a failure.

Further, the final diagnosis result (output) of the N pieces of data may be determined from the probability value (or predicted value) before the failure diagnosis model 15B outputs failure or sound for each data. . For example, the average of probability values (or predicted values) is used.

Next, with reference to FIG. 9, the operation of the failure diagnosis device 10 according to the first embodiment will be described.

FIG. 9 is a flowchart showing an example of the flow of processing by the failure diagnosis program 15A according to the first embodiment. Note that in the example of FIG. 9, the number of data acquired by the retry operation of the CGS 40 is two, but the number of data may be three or more.

First, the fault diagnosis program 15A stored in the storage unit 15 is activated by the CPU 11, and each step shown below is executed.

In step S111 of FIG. 9, the CPU 11 acquires a plurality of pieces of operation history data (here, data 1 and data 2) transmitted from the control device 41 of the CGS 40.

In step S112, the CPU 11 processes the data 1 acquired in step S111 into data that can be input into the failure diagnosis model 15B for each functional unit. Processing here means deleting part of the data, extracting part of the data, adding new data, etc. to the data 1. The processing also includes processing the data format of the data 1 into a format suitable for each fault diagnosis model. Note that if the operation history data is used as is, the processing is omitted.

In step S113, the CPU 11 inputs the data 1 processed in step S112 to each of the failure diagnosis models 15B for each functional unit.

In step S114, the CPU 11 stores the determination results output from each of the failure diagnosis models 15B for each functional unit in the storage unit 15.

In step S115, the CPU 11 processes the data 2 acquired in step S111 into data that can be input into the failure diagnosis model 15B for each functional unit. Note that if the operation history data is used as is, the processing is omitted.

In step S116, the CPU 11 inputs the data 2 processed in step S115 to each of the failure diagnosis models 15B for each functional unit.

In step S117, the CPU 11 stores in the storage unit 15 the determination results output from each of the failure diagnosis models 15B for each functional unit.

In step S118, the CPU 11 identifies a determination pattern from the determination result of data 1 held in the storage unit 15 in step S114 and the determination result of data 2 held in the storage unit 15 in step S117.

In step S119, the CPU 11 refers to, for example, the first data table 15C shown in FIG. The data is output to the storage unit 15, and a series of processes by the failure diagnosis program 15A are completed.

Next, with reference to FIG. 10, learning processing by the fault diagnosis device 10 according to the first embodiment will be specifically described.

FIG. 10 is a flowchart showing an example of the flow of learning processing by the failure diagnosis program 15A according to the first embodiment.

First, the fault diagnosis program 15A stored in the storage unit 15 is activated by the CPU 11, and each step shown below is executed.

In step S121 in FIG. 10, the CPU 11 acquires past operation history data as learning data.

In step S122, the CPU 11 processes the operation history data acquired as learning data in step S121 into data that can be input to the learning model. Note that if the operation history data is used as is, the processing is omitted.

In step S123, the CPU 11 inputs the data processed in step S122 together with correct data (for example, failure, sound, suspected failure) to the learning model for each functional unit.

In step S124, the CPU 11 determines whether machine learning has been completed for a predetermined number of data. If it is determined that the machine learning has ended (in the case of an affirmative determination), the process moves to step S125, and if it is determined that the machine learning has not ended (in the case of a negative determination), the process returns to step S121 and repeats the process.

In step S125, the CPU 11 stores the learned model generated by machine learning in the storage unit 15 as the fault diagnosis model 15B, and ends the series of processing by the fault diagnosis program 15A.

Note that, in this embodiment, a case has been described in which the failure diagnosis device 10 is implemented as an external server device, but the present invention is not limited to this. The failure diagnosis device 10 may be realized as the control device 41 of the CGS 40.

As described above, according to the present embodiment, when a plurality of errors are detected by the retry operation of the CGS 40, each operation history data at the time of the plurality of error detections is separately input to the fault diagnosis model, and the fault diagnosis is performed. is executed. Therefore, diagnostic accuracy is improved and erroneous determinations are suppressed.

[Second embodiment]
In the first embodiment, a mode has been described in which a plurality of pieces of operation history data obtained from the CGS 40 are input into all of the failure diagnosis models 15B for each functional unit. In this embodiment, path diagnosis according to the type of error in the CGS 40 is performed on at least one of the gas path 52, the reformed water path 53, and the air path 54, and narrows down the paths that may have a failure. A mode in which only the failure diagnosis model 15B related to the narrowed-down routes is selectively used will be described.

FIG. 11 is a block diagram showing an example of the functional configuration of a failure diagnosis device 10A according to the second embodiment.

As shown in FIG. 11, the CPU 11 of the failure diagnosis device 10A according to the present embodiment writes the failure diagnosis program 15A stored in the storage unit 15 into the RAM 13 and executes the acquisition unit 11A, the estimation unit 11B, It functions as a learning section 11C, an output section 11D, and a route diagnosis section 11E. Note that the same components as those of the failure diagnosis apparatus 10 described in the first embodiment are given the same reference numerals, and repeated explanation thereof will be omitted.

When detecting an error in the fuel cell unit 50, the route diagnosis section 11E specifies route diagnosis according to the error code included in the operation history data from the CGS 40. The route diagnosis unit 11E then performs route diagnosis on at least one of the gas route 52, the reformed water route 53, and the air route 54 according to the identified error code, and identifies routes that may have a failure. Refine. Note that the gas path 52, reformed water path 53, and air path 54 are paths provided in the fuel cell unit 50 and connected to the fuel cell module 51, as described above.

The estimation unit 11B selects a failure diagnosis model 15B related to the route narrowed down by the route diagnosis unit 11E from among the failure diagnosis models 15B for each functional unit stored in the storage unit 15. Then, the estimation unit 11B separately inputs the plurality of operation history data acquired by the acquisition unit 11A or the plurality of data obtained by processing the plurality of operation history data to the selected failure diagnosis model 15B. Then, the estimating unit 11B estimates the presence or absence of a failure for each functional unit of the CGS 40 using a plurality of determination results separately output from the failure diagnosis model 15B.

Further, the route diagnosis unit 11E may perform control to notify the result of route narrowing by route diagnosis together with an error code indicating the type of error. The destination of the error code is, for example, the maintenance terminal 70 held by the worker 60.

Further, the route diagnosis section 11E performs route diagnosis for at least one of the gas route 52, the reformed water route 53, and the air route 54 using a method according to the temperature range of the fuel cell unit 50.

Here, the storage unit 15 according to this embodiment stores a third data table 15E and a fourth data table 15F. These third data table 15E and fourth data table 15F can be referenced by the route diagnosis section 11E.

FIG. 12 is a diagram showing an example of the third data table 15E according to this embodiment.

The third data table 15E shown in FIG. 12 is a data table in which routes with a possibility of failure are specified and classified in advance for each error type (error codes A to H). Error code A is a code that targets the gas path 52, reformed water path 53, and air path 54, and is an example of the first error code. Error code B is a code that targets the gas path 52 and reformed water path 53, and is an example of a second error code. Error code C is a code that targets the gas path 52 and air path 54, and is an example of a third error code. Error code E is a code that targets the reformed water path 53 and air path 54, and is an example of the fourth error code. These error codes A, B, C, and E all target two or more routes. These error codes A, B, C, and E are associated with a route diagnosis method for narrowing down the route. Specifically, error code A is associated with route diagnosis A, error code B is associated with route diagnosis B, error code C is associated with route diagnosis C, and error code E is associated with route diagnosis. E is associated. Note that specific route diagnosis methods for these route diagnoses A, B, C, and E will be described later.

Note that since error code D targets only the gas path 52, the cause of the error is identified as the gas path 52. Similarly, error code F targets only the reformed water route 53, so the cause of the error is identified as the reformed water route 53, and error code G targets only the air route 54, so the error The cause is identified as the air path 54. Further, error code H is an error unrelated to each path of gas, reformed water, and air. In the example of FIG. 12, route diagnosis is not performed for these error codes D, F, G, and H, but route diagnosis may be performed for error codes D, F, and G.

FIG. 13 is a diagram showing an example of the fourth data table 15F according to this embodiment.

The fourth data table 15F shown in FIG. 13 is a data table that defines the classification of temperature zones and the route diagnosis method for reformed water, gas, and air in each temperature zone. The temperature zones of the fuel cell unit 50 are a temperature zone [A] which is an example of the first temperature zone, a temperature zone [B] which is an example of the second temperature zone, and a temperature zone [C] which is an example of the third temperature zone. , and a temperature zone [d] which is an example of the fourth temperature zone.

The maximum temperature of the fuel cell module 51 is defined as the maximum temperature T1 [°C], and the temperature at which carbon precipitation occurs (for example, 500°C) when gas is introduced into the reformer 51B (reforming catalyst) is defined as the carbon deposition temperature T2 [°C]. , the temperature at which the fuel cell is reoxidized by introducing air into the fuel cell stack 51A (for example, 300°C) is the reoxidation temperature T3 [°C], and the temperature at which the reformed water introduced into the reformer 51B is vaporized. (For example, 100° C.) is the vaporization temperature T4 [° C.], the temperature zone [a] is higher than the carbon precipitation temperature T2 and below the maximum temperature T1. The temperature zone [b] is a temperature zone higher than the reoxidation temperature T3 and lower than the carbon precipitation temperature T2. Temperature zone [c] is a temperature zone higher than vaporization temperature T4 and lower than reoxidation temperature T3. The temperature zone [d] is a temperature zone below the vaporization temperature T4.

In the gas route diagnosis method, when the temperature of the fuel cell unit 50 is in the temperature range [A], reformed water and gas are introduced, and the temperature of the fuel cell unit 50 is in the temperature range [B] or the temperature range [C]. Alternatively, there is a method of introducing gas when the temperature is in the temperature range [d]. The reason why reformed water is introduced in temperature range [A] is to prevent carbon precipitation. Note that in the temperature zone [B] and temperature zone [C], reformed water and gas may be introduced. Note that, as the temperature of the fuel cell unit 50, at least one of the stack temperature, which is the temperature of the fuel cell stack 51A, and the reforming temperature, which is the temperature of the reformer 51B, is used. For example, the higher temperature of the stack temperature and the reforming temperature may be set as the temperature of the fuel cell unit 50, or the average temperature of the stack temperature and the reforming temperature may be set as the temperature of the fuel cell unit 50. Further, temperature information of the fuel cell unit 50 is acquired from the control device 41 of the CGS 40. The temperature information of the fuel cell unit 50 may be included in the operation history data transmitted from the control device 41 to the failure diagnosis device 10A, for example.

The method for diagnosing the reformed water path is to introduce reformed water and check the temperature of the fuel cell unit 50 when the temperature of the fuel cell unit 50 is in the temperature range [A], the temperature range [B], or the temperature range [C]. This is a method that makes diagnosis impossible when the temperature is in the temperature range [d].

In the air path diagnosis method, when the temperature of the fuel cell unit 50 is in the temperature range [A] or the temperature range [B], reformed water, gas, and air are introduced, and the temperature of the fuel cell unit 50 is in the temperature range [B]. This is a method of introducing air when the temperature is in [C] or [D]. The reason why reformed water, gas, and air are introduced in temperature zone [a] or temperature zone [b] is to prevent reoxidation of the cell. Note that in temperature zone [A], reformed water and air may be introduced. In the temperature zone [B], reformed water and air, or gas and air may be introduced. In temperature zone [C], reformed water, gas, and air, reformed water and air, or gas and air may be introduced.

Next, with reference to FIG. 14, the operation of the failure diagnosis device 10A according to the second embodiment will be described.

FIG. 14 is a flowchart showing an example of the flow of processing by the failure diagnosis program 15A according to the second embodiment.

First, the fault diagnosis program 15A stored in the storage unit 15 is activated by the CPU 11, and each step shown below is executed.

In step S131 of FIG. 14, the CPU 11 acquires a plurality of operation history data (here, data 1 and data 2) transmitted from the control device 41 of the CGS 40.

In step S132, the CPU 11 extracts a common error code from data 1 and data 2 acquired in step S131.

In step S133, the CPU 11 determines whether the error code extracted in step S132 is error code A. If it is determined that the error code is A (in the case of an affirmative determination), the process moves to step S134, and if it is determined that the error code is not the error code A (in the case of a negative determination), the process moves to step S135.

In step S134, the CPU 11, for example, refers to the third data table 15E shown in FIG. Then, the process moves to step S141. Note that a specific method for this route diagnosis A will be described later.

On the other hand, in step S135, the CPU 11 determines whether the error code extracted in step S132 is error code B. If it is determined that the error code is B (in the case of an affirmative determination), the process proceeds to step S136, and if it is determined that the error code is not B (in the case of a negative determination), the process proceeds to step S137.

In step S136, the CPU 11 refers to the third data table 15E shown in FIG. 12 described above, performs route diagnosis B associated with error code B, and proceeds to step S141. Note that a specific method for this route diagnosis B will be described later.

On the other hand, in step S137, the CPU 11 determines whether the error code extracted in step S132 is error code C. If it is determined that the error code is C (in the case of an affirmative determination), the process proceeds to step S138, and if it is determined that the error code is not C (in the case of a negative determination), the process proceeds to step S139.

In step S138, the CPU 11 refers to the third data table 15E shown in FIG. 12 described above, performs route diagnosis C associated with the error code C, and proceeds to step S141. Note that a specific method for this route diagnosis C will be described later.

On the other hand, in step S139, the CPU 11 determines whether the error code extracted in step S132 is error code E. If it is determined that the error code is E (in the case of a positive determination), the process moves to step S140, and if it is determined that the error code is not E (in the case of a negative determination), an error other than error code A, B, C, or E is detected. The process moves to step S141 without performing route diagnosis for codes D, F, G, and H.

In step S140, the CPU 11 refers to the third data table 15E shown in FIG. 12 described above, performs route diagnosis E associated with the error code E, and proceeds to step S141. Note that a specific method for this route diagnosis E will be described later.

In step S141, the CPU 11 identifies a failure route that is the result of narrowing down by the route diagnosis in step S134, step S136, step S138, or step S140, or identifies an error code D other than error code A, B, C, or E. Identify the failure paths corresponding to F, G, and H.

In step S142, the CPU 11 selects a fault diagnosis model 15B related to the fault path identified in step S141 from among the fault diagnosis models 15B for each functional unit stored in the storage unit 15, and only the selected fault diagnosis model 15B is used. A fault diagnosis is performed using the fault diagnosis program 15A, and a series of processing by this fault diagnosis program 15A is completed.

Next, with reference to FIG. 15, a specific method of route diagnosis A according to step S134 in FIG. 14 will be described.

FIG. 15 is a flowchart illustrating an example of the process flow of route diagnosis A according to the second embodiment. Note that in the route diagnosis A according to the present embodiment, route diagnosis is performed in the order of the reformed water route 53, the gas route 52, and the air route 54, as an example.

In step S151 of FIG. 15, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine whether it is [d]. Note that, as described above, at least one of the stack temperature and the reforming temperature is used as the temperature of the fuel cell unit 50. If it is determined that the temperature of the fuel cell unit 50 is in the temperature zone [A], the temperature zone [B], or the temperature zone [C] (in the case of A, B, or C), the process moves to step S152, and the temperature of the fuel cell unit 50 is If it is determined that the temperature is in the temperature range [d] (in the case of d), the process moves to step S153.

In step S152, the CPU 11 controls the CGS 40 to introduce reforming water into the reforming water path 53, and proceeds to step S154.

On the other hand, in step S153, the CPU 11 adds a route determination impossible tag to the reformed water route 53, and proceeds to step S159.

In step S154, the CPU 11 determines whether the reformed water path 53 is normal according to a predetermined criterion. If it is determined that the reformed water path 53 is normal (in the case of a positive determination), the process moves to step S155, and if it is determined that the reformed water path 53 is not normal, that is, abnormal (in the case of a negative determination) ), the process moves to step S156.

In step S155, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine which of the following. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] (in the case of A), the process moves to step S158, and the temperature of the fuel cell unit 50 is in the temperature range [B] or in the temperature range [C] or in the temperature range [C]. If it is determined that it is band [D] (in the case of B, C, or D), the process moves to step S159.

On the other hand, in step S156, the CPU 11 adds a route abnormality tag to the reformed water route 53.

In step S157, the CPU 11 waits until the temperature of the fuel cell unit 50 falls below the temperature range [b], and then proceeds to step S159.

In step S158, the CPU 11 controls introducing reformed water into the reformed water path 53 and gas into the gas path 52, and proceeds to step S160.

On the other hand, in step S159, the CPU 11 performs control to introduce gas into the gas path 52, and proceeds to step S160.

In step S160, the CPU 11 determines whether the gas path 52 is normal according to a predetermined criterion. If it is determined that the gas path 52 is normal (in the case of a positive determination), the process moves to step S161, and if it is determined that the gas path 52 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S165. to move to.

In step S161, the CPU 11 determines whether the reformed water path 53 is normal according to a predetermined criterion. If it is determined that the reformed water path 53 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S162, and if it is determined that the reformed water path 53 is normal (in the case of an affirmative determination) ), the process moves to step S164.

In step S162, the CPU 11 waits until the temperature of the fuel cell unit 50 falls below the temperature range [b].

In step S163, the CPU 11 performs control to introduce gas into the gas path 52 and air into the air path 54, and proceeds to step S170.

On the other hand, in step S164, the CPU 11 refers to the fourth data table 15F shown in FIG. (d)]. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] or [B] (in the case of A or B), the process moves to step S168, and the temperature of the fuel cell unit 50 is in the temperature range [C]. Alternatively, if it is determined that the temperature is in the temperature range [d] (in the case of c or d), the process moves to step S169.

On the other hand, in step S165, the CPU 11 adds a route abnormality tag to the gas route 52.

In step S166, the CPU 11 determines whether the reformed water path 53 is normal according to a predetermined criterion. If it is determined that the reformed water path 53 is normal (in the case of a positive determination), the process moves to step S164, and if it is determined that the reformed water path 53 is not normal, that is, abnormal (in the case of a negative determination) ), the process moves to step S167.

In step S167, the CPU 11 waits until the temperature of the fuel cell unit 50 falls below the temperature range [c], and then proceeds to step S169.

In step S168, the CPU 11 controls introducing reformed water into the reformed water path 53, gas into the gas path 52, and air into the air path 54, and proceeds to step S170.

On the other hand, in step S169, the CPU 11 performs control to introduce air into the air path 54, and proceeds to step S170.

In step S170, the CPU 11 determines whether the air path 54 is normal according to a predetermined criterion. If it is determined that the air path 54 is normal (in the case of a positive determination), the process moves to step S172, and if it is determined that the air path 54 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S171. to move to.

In step S171, the CPU 11 adds a route abnormality tag to the air path 54, and proceeds to step S172.

In step S172, the CPU 11 determines the error, that is, determines the route to which the route abnormality tag is attached among the reformed water route 53, the gas route 52, and the air route 54 as a route with a possibility of failure. , the process returns to step S141 in FIG.

Next, with reference to FIG. 16, a specific method of route diagnosis B related to step S136 in FIG. 14 will be described.

FIG. 16 is a flowchart illustrating an example of the process flow of route diagnosis B according to the second embodiment. Note that in route diagnosis B according to the present embodiment, route diagnosis is performed in the order of the reformed water route 53 and the gas route 52, as an example.

In step S181 in FIG. 16, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine whether it is [d]. If it is determined that the temperature of the fuel cell unit 50 is in the temperature zone [A], the temperature zone [B], or the temperature zone [C] (in the case of A, B, or C), the process moves to step S182, and the temperature of the fuel cell unit 50 is If it is determined that the temperature is in the temperature range [d] (in the case of d), the process moves to step S183.

In step S182, the CPU 11 controls introducing reformed water into the reformed water path 53, and proceeds to step S184.

On the other hand, in step S183, the CPU 11 adds a route determination impossible tag to the reformed water route 53, and proceeds to step S189.

In step S184, the CPU 11 determines whether the reformed water path 53 is normal according to a predetermined criterion. If it is determined that the reformed water path 53 is normal (in the case of a positive determination), the process moves to step S185, and if it is determined that the reformed water path 53 is not normal, that is, abnormal (in the case of a negative determination) ), the process moves to step S186.

In step S185, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine which of the following. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] (in the case of A), the process moves to step S188, and the temperature of the fuel cell unit 50 is in the temperature range [B] or in the temperature range [C] or in the temperature range [C]. If it is determined that it is band [D] (in the case of B, C, or D), the process moves to step S189.

On the other hand, in step S186, the CPU 11 adds a route abnormality tag to the reformed water route 53.

In step S187, the CPU 11 waits until the temperature of the fuel cell unit 50 falls below the temperature range [b], and then proceeds to step S189.

In step S188, the CPU 11 controls introducing reformed water into the reformed water path 53 and gas into the gas path 52, and proceeds to step S190.

On the other hand, in step S189, the CPU 11 performs control to introduce gas into the gas path 52, and proceeds to step S190.

In step S190, the CPU 11 determines whether the gas path 52 is normal according to a predetermined criterion. If it is determined that the gas path 52 is normal (in the case of a positive determination), the process moves to step S192, and if it is determined that the gas path 52 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S191. to move to.

In step S191, the CPU 11 attaches a route abnormality tag to the gas route 52, and proceeds to step S192.

In step S192, the CPU 11 determines the error, that is, determines the route to which the route abnormality tag has been attached among the reformed water route 53 and the gas route 52 as a route with a possibility of failure, and steps The process returns to S141.

Next, with reference to FIG. 17, a specific method of route diagnosis C according to step S138 in FIG. 14 will be described.

FIG. 17 is a flowchart illustrating an example of the process flow of route diagnosis C according to the second embodiment. Note that in the route diagnosis C according to the present embodiment, route diagnosis is performed in the order of the gas route 52 and the air route 54, as an example.

In step S201 in FIG. 17, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine whether it is [d]. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] (in the case of A), the process moves to step S202, and the temperature of the fuel cell unit 50 is in the temperature range [B] or in the temperature range [C] or in the temperature range [C]. If it is determined that it is band [d] (in the case of b, c, or d), the process moves to step S203.

In step S202, the CPU 11 controls introducing reformed water into the reformed water path 53 and gas into the gas path 52, and proceeds to step S204.

On the other hand, in step S203, the CPU 11 performs control to introduce gas into the gas path 52, and proceeds to step S204.

In step S204, the CPU 11 determines whether the gas path 52 is normal according to a predetermined criterion. If it is determined that the gas path 52 is normal (in the case of a positive determination), the process moves to step S205, and if it is determined that the gas path 52 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S206. to move to.

In step S205, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine which of the following. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] or [B] (in the case of A or B), the process moves to step S207, and the temperature of the fuel cell unit 50 is in the temperature range [C]. Alternatively, if it is determined that the temperature is in the temperature range [d] (in the case of c or d), the process moves to step S208.

On the other hand, in step S206, the CPU 11 attaches a route abnormality tag to the gas route 52, and proceeds to step S205.

In step S207, the CPU 11 controls introducing reformed water into the reformed water path 53, gas into the gas path 52, and air into the air path 54, and proceeds to step S209.

On the other hand, in step S208, the CPU 11 performs control to introduce air into the air path 54, and proceeds to step S209.

In step S209, the CPU 11 determines whether the air path 54 is normal according to a predetermined criterion. If it is determined that the air path 54 is normal (in the case of a positive determination), the process moves to step S211, and if it is determined that the air path 54 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S210. to move to.

In step S210, the CPU 11 adds a route abnormality tag to the air path 54, and proceeds to step S211.

In step S211, the CPU 11 determines the error, that is, determines the route to which the route abnormality tag is attached among the gas route 52 and the air route 54 as a route with a possibility of failure, and proceeds to step S141 in FIG. Return.

Next, with reference to FIG. 18, a specific method of route diagnosis E according to step S140 in FIG. 14 will be described.

FIG. 18 is a flowchart illustrating an example of the process flow of route diagnosis E according to the second embodiment. In the route diagnosis E according to the present embodiment, the route diagnosis is performed in the order of the reformed water route 53 and the air route 54, as an example.

In step S221 of FIG. 18, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine whether it is [d]. If it is determined that the temperature of the fuel cell unit 50 is in the temperature zone [A], the temperature zone [B], or the temperature zone [C] (in the case of A, B, or C), the process moves to step S222, and the temperature of the fuel cell unit 50 is If it is determined that the temperature is in the temperature range [d] (in the case of d), the process moves to step S223.

In step S222, the CPU 11 controls introducing reformed water into the reformed water path 53, and proceeds to step S224.

On the other hand, in step S223, the CPU 11 adds a route determination impossible tag to the reformed water route 53, and proceeds to step S228.

In step S224, the CPU 11 determines whether the reformed water path 53 is normal according to a predetermined criterion. If it is determined that the reformed water path 53 is normal (in the case of a positive determination), the process moves to step S225, and if it is determined that the reformed water path 53 is not normal, that is, abnormal (in the case of a negative determination) ), the process moves to step S226.

In step S225, the CPU 11 refers to the fourth data table 15F shown in FIG. Determine which of the following. If it is determined that the temperature of the fuel cell unit 50 is in the temperature range [A] or [B] (in the case of A or B), the process moves to step S227, and the temperature of the fuel cell unit 50 is in the temperature range [C]. Alternatively, if it is determined that the temperature is in the temperature range [d] (in the case of c or d), the process moves to step S228.

On the other hand, in step S226, the CPU 11 adds a route abnormality tag to the reformed water route 53, and proceeds to step S228.

In step S227, the CPU 11 controls introducing reformed water into the reformed water path 53, gas into the gas path 52, and air into the air path 54, and proceeds to step S229.

On the other hand, in step S228, the CPU 11 performs control to introduce air into the air path 54, and proceeds to step S229.

In step S229, the CPU 11 determines whether the air path 54 is normal according to a predetermined criterion. If it is determined that the air path 54 is normal (in the case of a positive determination), the process moves to step S231, and if it is determined that the air path 54 is not normal, that is, abnormal (in the case of a negative determination), the process moves to step S230. to move to.

In step S230, the CPU 11 adds a route abnormality tag to the air path 54, and proceeds to step S231.

In step S231, the CPU 11 determines the error, that is, determines the route to which the route abnormality tag has been attached among the reformed water route 53 and the air route 54 as a route with a possibility of failure, and steps The process returns to S141.

In this embodiment, the case where the failure diagnosis device 10A is implemented as an external server device has been described, but the present invention is not limited to this. The failure diagnosis device 10A may be realized as the control device 41 of the CGS 40.

As described above, according to the present embodiment, it is only necessary to narrow down the failure paths and apply only the failure diagnosis model related to the narrowed down failure paths. Therefore, failure diagnosis can be performed efficiently and accurately.

The above embodiments have been described by exemplifying a fault diagnosis device and a fault diagnosis system, but the embodiments may also be in the form of a program for causing a computer to execute the functions of each part included in the fault diagnosis device. Embodiments may be in the form of a computer readable storage medium storing this program.

In addition, the configurations of the failure diagnosis device and failure diagnosis system described in the above embodiments are merely examples, and may be changed according to the situation without departing from the spirit of the invention.

Furthermore, the process flow of the program described in the above embodiment is only an example, and unnecessary steps may be deleted, new steps may be added, or the processing order may be changed without departing from the main purpose. good.

Further, in the above embodiment, a case has been described in which the processing according to the embodiment is realized by a software configuration using a computer by executing a program, but the present invention is not limited to this. The embodiments may be realized by, for example, a hardware configuration or a combination of a hardware configuration and a software configuration.

10, 10A Failure diagnosis device 11, 42 CPU
11A Acquisition unit 11B Estimation unit 11C Learning unit 11D Output unit 11E Route diagnosis unit 12, 43 ROM
13, 44 RAM
14, 45 I/O
15, 46 Storage section 15A Failure diagnosis program 15B Failure diagnosis model 15C First data table 15D Second data table 15E Third data table 15F Fourth data table 16 Display section 17 Operation section 18, 47 Communication section 30 User's home 40 CGS
41 Control device 46A Control program 48 Device I/F
49 CGS main body 50 Fuel cell unit 51 Fuel cell module 51A Fuel cell stack 51B Reformer 52 Gas path 53 Reformed water path 54 Air path 60 Worker 70 Maintenance terminal 100 Failure diagnosis system

Claims (13)

When the fuel cell cogeneration system detects an operational abnormality, the fuel cell cogeneration system performs a retry operation, and if multiple abnormalities are detected as a result of the retry operation, the fuel cell cogeneration system , an acquisition unit that acquires a plurality of operation history data regarding the operation history of the fuel cell cogeneration system corresponding to each of the plurality of abnormalities;
It is generated by multivariate analysis or machine learning by associating a group of operation history data obtained in the past with the functional parts of the fuel cell cogeneration system, and the failure occurs for each functional part of the fuel cell cogeneration system. separately inputting a plurality of operation history data acquired by the acquisition unit or a plurality of data obtained by processing the plurality of operation history data into a failure diagnosis model for each functional unit that determines the presence or absence of the an estimation unit that estimates the presence or absence of a failure for each functional unit of the fuel cell cogeneration system using a plurality of determination results separately output from the failure diagnosis model;
A fault diagnosis device equipped with The estimating unit estimates the functional unit associated with the fault diagnosis model to be faulty when the determination results for each of a plurality of data separately input to the fault diagnosis model are faulty, and the estimation unit estimates the functional unit associated with the fault diagnosis model as faulty. If the judgment results for each of the plurality of data input separately to the diagnostic model are all healthy, the functional unit associated with the failure diagnosis model is estimated to be healthy, and the functional unit is input separately to the failure diagnosis model. The failure diagnosis device according to claim 1, wherein when one of the determination results for each of the plurality of data is faulty and the other is healthy, the functional unit associated with the failure diagnosis model is presumed to be faulty. When the number of retry operations is two or more, the estimation unit performs the fault diagnosis based on the number of times of failure and soundness determination, which is a determination result for each of a plurality of data input separately to the failure diagnosis model. The failure diagnosis device according to claim 1, wherein the functional unit associated with the model is estimated to be faulty, healthy, or suspected to be faulty. When the number of retry operations is two or more, the estimating unit calculates the retry operation based on determination results for each of a plurality of data input separately to the failure diagnosis model and a precision rate of the failure diagnosis model at the time of failure determination. The fault diagnosis device according to claim 1, wherein the functional unit associated with the fault diagnosis model is estimated to be faulty, healthy, or suspected to be faulty based on the derived fault score. When the number of retry operations is two or more, the estimating unit calculates a determination result for each of a plurality of data separately input to the failure diagnosis model, and a conformity rate or failure of the failure diagnosis model at the time of health determination. The fault diagnosis device according to claim 1, wherein the functional unit associated with the fault diagnosis model is estimated to be faulty, healthy, or suspected to be faulty based on a soundness score derived from the detection recall rate. When the number of retry operations is two or more, the estimating unit calculates the retry operation based on determination results for each of a plurality of data input separately to the failure diagnosis model and a precision rate of the failure diagnosis model at the time of failure determination. The derived fault score, the judgment result for each of the plurality of data input separately to the fault diagnosis model, and the precision rate or recall rate of fault detection of the fault diagnosis model when determining the soundness of the fault diagnosis model. The failure diagnosis device according to claim 1, wherein the functional unit associated with the failure diagnosis model is estimated to be faulty, healthy, or suspected to be faulty based on the score. The failure diagnosis device according to any one of claims 1 to 6, wherein the number of times the retry operation is performed is predetermined according to an error code representing the type of abnormality. The fuel cell cogeneration system includes a fuel cell unit that is provided with a fuel cell module that generates electricity, and that is provided with a gas path, a reformed water path, and an air path that are connected to the fuel cell module,
When an abnormality in the operation of the fuel cell unit is detected, path diagnosis according to the type of abnormality is performed on at least one of the gas path, the reformed water path, and the air path, and the malfunction is detected. It is further equipped with a route diagnosis section that narrows down possible routes.
The estimation unit selects a failure diagnosis model related to the route narrowed down by the route diagnosis unit from among the failure diagnosis models for each of the functional units, and uses the selected failure diagnosis model to estimate the performance of the fuel cell cogeneration system. The failure diagnosis device according to any one of claims 1 to 7, wherein the presence or absence of a failure is estimated for each functional unit. The operation history data includes an error code representing the type of abnormality,
The error code includes a first error code that targets the gas route, the reformed water route, and the air route, a second error code that targets the gas route and the reformed water route, and a second error code that targets the gas route and the reformed water route. The failure diagnosis device according to claim 8, which is one of a third error code targeted at the air path and a fourth error code targeted at the reformed water path and the air path. The route diagnosis section performs the route diagnosis for at least one of the gas route, the reformed water route, and the air route using a method according to a temperature range of the fuel cell unit. The failure diagnosis device according to item 9. The fuel cell module includes a fuel cell stack in which a plurality of fuel cells having an electrolyte layer, a fuel electrode, and an air electrode are stacked, and a reformer for reforming a gas containing a hydrocarbon raw material,
The temperature zone of the fuel cell unit is a first temperature zone that is higher than the carbon deposition temperature, which is the temperature at which carbon deposition occurs when gas is introduced into the reformer, and is lower than or equal to the maximum temperature of the fuel cell module, and the fuel cell stack. a second temperature zone higher than the reoxidation temperature, which is the temperature at which reoxidation of the fuel cell occurs by introducing air into the fuel cell, and lower than the carbon deposition temperature; and a second temperature zone at which the reformed water introduced into the reformer vaporizes. a third temperature zone higher than a certain vaporization temperature and below the reoxidation temperature, and a fourth temperature zone below the vaporization temperature,
The route diagnosis method for the gas route includes introducing reformed water and gas when the temperature of the fuel cell unit is in the first temperature range, and introducing reformed water and gas when the temperature of the fuel cell unit is in the second temperature range or A method of introducing gas when the temperature is in the third temperature zone or the fourth temperature zone,
The route diagnosis method for the reformed water route includes introducing reformed water when the temperature of the fuel cell unit is in the first temperature zone, the second temperature zone, or the third temperature zone; A method of disabling diagnosis when the temperature of the fuel cell unit is in the fourth temperature range,
The route diagnosis method for the air route includes introducing at least reformed water and air when the temperature of the fuel cell unit is in the first temperature range, and introducing the reformed water and air when the temperature of the fuel cell unit is in the second temperature range. In this case, at least one of reformed water and gas and air are introduced, and the air is introduced when the temperature of the fuel cell unit is in the third temperature zone or the fourth temperature zone. The failure diagnosis device according to claim 10. fuel cell cogeneration system,
a fault diagnosis device connected to the fuel cell cogeneration system;
Equipped with
The fuel cell cogeneration system includes:
When an abnormality in the operation of the own system is detected, a retry operation is performed on the own system, and if multiple abnormalities are detected as a result of the retry operation, the fault diagnosis device is notified of the multiple abnormalities. Send multiple pieces of operation history data related to the operation history of the own system, each corresponding to
The failure diagnosis device includes:
an acquisition unit that acquires the plurality of operation history data transmitted from the fuel cell cogeneration system;
It is generated by multivariate analysis or machine learning by associating a group of operation history data obtained in the past with the functional parts of the fuel cell cogeneration system, and the failure occurs for each functional part of the fuel cell cogeneration system. separately inputting a plurality of operation history data acquired by the acquisition unit or a plurality of data obtained by processing the plurality of operation history data into a failure diagnosis model for each functional unit that determines the presence or absence of the an estimation unit that estimates the presence or absence of a failure for each functional unit of the fuel cell cogeneration system using a plurality of determination results separately output from the failure diagnosis model;
A fault diagnosis system equipped with A fault diagnosis program for causing a computer to function as each part included in the fault diagnosis apparatus according to any one of claims 1 to 11.