Diagnostic method, diagnostic processing device, terminal, and, diagnostic system

Abstract

To provide a technique for evaluating a portion of an appliance which produces an abnormal output.SOLUTION: A diagnostic processing device comprises a processor and a communication device. In a normal operation state of a device, the processor generates a prediction model that predicts a first physical quantity measured by a first sensor of a plurality of sensors based on data obtained from the plurality of sensors included in the device via the communication device. The processor determines an abnormality in information about the first physical quantity by using a prediction value of the first physical quantity predicted by using the prediction model and a measured value of the first physical quantity measured by the first sensor. When the processor determines that the information is abnormal, the processor calculates a contribution value for the degree of abnormality in the information for respective sensors other than the first sensor. The processor uses the contribution value to estimate an abnormal output value of a portion of the device and to estimate an abnormal portion of the device.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a diagnostic method, a diagnostic processing device, a terminal, and a diagnostic system. [Background technology]

[0002] There are known techniques for estimating power consumption and predicting component failures. Patent Document 1 discloses a power consumption estimation device that estimates the power consumption of each device or each purpose from the total power consumption by nonnegative matrix factorization.

[0003] Patent Document 2 focuses on the harmonic current pattern generated by electrical equipment installed at an electric power consumer's premises, and discloses a technology for calculating the phase difference between the fundamental wave of the total load current and the harmonic current and voltage from the total load current and voltage measured near the power supply line entrance, and estimating the electrical equipment used indoors and the power consumption of each electrical equipment from the pattern.

[0004] Patent Document 3 discloses a failure prediction device including a machine learning device including a state observation unit that observes operation status data indicating the operation status of a managed device and device configuration data indicating the device configuration of the managed device as state variables indicating the current state of the environment, a label data acquisition unit that acquires maintenance history data indicating the maintenance history of the managed device as label data, and a learning unit that uses the state variables and the label data to associate and learn the failure time of a printed board constituting the managed device with the operation status data and the device configuration data. Patent Document 3 discloses that a failure estimation model is updated from time to time by machine learning, thereby making it possible to perform highly accurate failure prediction. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-205684 A [Patent Document 2] International Publication No. 2001 / 077696 [Patent Document 3] JP 2019-8675 A Summary of the Invention [Problem to be solved by the invention]

[0006] A maintenance method is known in which the condition of equipment (devices) is monitored in real time to prevent breakdowns and maintenance is performed according to the condition. This method is sometimes called Condition Based Maintenance (CBM). Based on the concept of CBM, identifying a part of equipment that produces an abnormal output (for example, a part that produces more output than normal) is considered to have the following advantages, for example. For example, it is considered possible to use the equipment with reduced running costs by adjusting the output of the part in question. Also, for example, it is considered possible to make the equipment usable by replacing only the part in question without replacing the entire equipment.

[0007] Therefore, there is a need to provide a technique for evaluating a portion of a device that produces an abnormal output. [Means for solving the problem]

[0008] According to a first aspect of the present invention, there is provided a diagnostic method as follows. The diagnostic method includes a prediction model generating step, an abnormality determining step, a contribution value calculating step, and an estimation step. In the prediction model generating step, a processor generates a prediction model for predicting a first physical quantity measured by a first sensor among a plurality of sensors provided in the device, based on data obtained from the plurality of sensors in the device when the device is in a normal operating state. In the abnormality determining step, the processor determines an abnormality in information related to the first physical quantity, using a predicted value of the first physical quantity predicted using the prediction model and an actual measured value of the first physical quantity measured by the first sensor. In the contribution value calculating step, when the processor determines that the information is abnormal, the processor calculates a contribution value to the abnormality degree of the information for each sensor other than the first sensor. In the estimation step, the processor uses the contribution value to estimate an abnormal output value of a portion of the device, and estimates an abnormal portion in the device.

[0009] According to a second aspect of the present invention, the following diagnostic processing device is provided. The diagnostic processing device includes a processor and a communication device. The processor generates a prediction model for predicting a first physical quantity measured by a first sensor among a plurality of sensors based on data obtained from a plurality of sensors included in the device in a normal operating state via the communication device. The processor determines an abnormality in information related to the first physical quantity using a predicted value of the first physical quantity predicted using the prediction model and an actual measurement value of the first physical quantity measured by the first sensor. When the processor determines that the information is abnormal, it calculates a contribution value to the degree of abnormality of the information for each sensor other than the first sensor. The processor uses the contribution value to estimate an abnormal output value of a part of the device and estimates an abnormal part in the device. Also, for example, a terminal is provided that provides a user with information based on data input from the diagnostic processing device via communication. Also, for example, a diagnostic system is provided that includes a diagnostic processing device that outputs information related to an estimated abnormal part in the device to the outside via the communication device, and a terminal that provides a user with information based on data input from the diagnostic processing device. Also, a diagnostic processing device may be provided that determines whether or not there is an abnormality in the information relating to the first physical quantity by using a predetermined threshold value. Effect of the Invention

[0010] According to the present invention, a technique for evaluating a portion of a device that produces an abnormal output is provided. Note that problems, configurations, and effects other than those described above will become apparent from the following description of the embodiment of the invention. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 illustrates an example of a configuration of a diagnostic system. [Diagram 2] 4 is a flowchart showing an example of an overall process in the present embodiment. [Diagram 3] FIG. 11 is a diagram illustrating an example of setting a virtual current based on a current abnormality degree. [Figure 4]FIG. 13 is a diagram illustrating an example of a correspondence table that associates parts of an apparatus with sensors. [Diagram 5] FIG. 11 is a diagram showing an example of information provided to a user. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiment is an example for explaining the present invention, and appropriate omissions and simplifications are made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. As examples of various information, the various information may be described using expressions such as "table," "list," and "queue," but the various information may be expressed using data structures other than these. For example, various information such as "XX table," "XX list," and "XX queue" may be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are mutually interchangeable. When there are multiple components having the same or similar functions, they may be described by using the same reference numerals with different subscripts, or when there is no need to distinguish between these multiple components, the subscripts may be omitted. In the embodiment, a process performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., CPU, GPU), and performs the process defined by the program while using a storage resource (e.g., memory) and an interface device (e.g., communication port), etc. Therefore, the subject of the process performed by executing the program may be the processor. Similarly, the subject of the process performed by executing the program may be a controller, device, system, computer, or node having a processor. The subject of the process performed by executing the program may be a calculation unit, and may include a dedicated circuit that performs a specific process. Here, the dedicated circuit is, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device), etc. The program may be installed in the computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and a storage resource for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0013] In the embodiment, an example of a technology is described in which, when it is determined that there is an abnormality in a sensor value based on data acquired from an apparatus (device) equipped with multiple sensors, a factor analysis is performed to quantitatively estimate an abnormal output value of a part of the apparatus, and an abnormal part of the apparatus (a part that produces an abnormal output) is estimated. Note that in the embodiment, an example in which a current value is acquired from a current sensor and a factor analysis is performed is described in detail. However, based on a similar method, a sensor value may be acquired from another type of sensor and a factor analysis may be performed to estimate an abnormal part of the apparatus.

[0014] First, an example of a diagnostic system will be described with reference to Fig. 1. As shown in Fig. 1, a diagnostic system 101 includes a diagnostic processing device 111 and an apparatus 131 (apparatus). Note that in this embodiment, the diagnostic processing device 111 is a computer that exists in a cloud environment, but may be a computer that exists in an on-premise environment.

[0015] The diagnostic system 101 also includes an information output device 151 used to provide information to a user. The information output device 151 is, for example, a user terminal (terminal). This user terminal is appropriately configured and connected to the diagnostic processing device 111 via communication. The diagnostic processing device 111 then outputs data related to the processing result to the information output device 151.

[0016] The diagnostic processing device 111 includes a processor 112, a memory 113, a storage 114, and a communication device 115. The processor 112 is an entity that executes a predetermined process, and is, for example, a CPU (Central Processing Unit). However, as long as it is capable of executing the predetermined process, it may be configured using other semiconductor devices.

[0017] The memory 113 is a main storage device, such as a random access memory (RAM). The processor 112 stores data in the memory 113 during processing.

[0018] The storage 114 is an auxiliary storage device, such as a hard disk drive (HDD), and can store, for example, data acquired from an external source, processing results, programs, and the like.

[0019] The device 131 is, for example, an industrial machine installed in a factory, and examples of the types of the device include a compressor, a water supply unit, an air shower, an inkjet printer, a hoist, etc. The device 131 is provided with various sensors according to the type, and the device 131 configures a sensor unit 132 using the respective sensors provided in the device 131.

[0020] In this embodiment, the diagnostic processing device 111 is configured to be able to acquire various information in the diagnostic system 101. The diagnostic processing device 111 collects, for example, sensor values ​​of sensors provided in the device 131 via a network.

[0021] The diagnostic processing device 111 also includes, as its functional components, a sensor value acquisition unit 121, a prediction model generation unit 122, an abnormality determination unit 123, a contribution value calculation unit 124, an abnormal part estimation unit 125, and an information output unit 126. Each of these units (121 to 126) is realized by the processor 112 storing a predetermined program in the memory 113 and executing data processing.

[0022] The sensor value acquisition unit 121 acquires the sensor values ​​of the sensors from each of the devices 131 .

[0023] The prediction model generating unit 122 generates a prediction model that predicts a normal value of the first sensor of the device 131. Here, the prediction model is a model that receives a sensor value of a sensor other than the first sensor of the device 131 as an input and outputs a predicted value of the first sensor. The prediction model generating unit 122 generates a prediction model by performing a learning process using a sensor value in a normal operating state of the device 131 as teacher data. In this embodiment, the first sensor is a current sensor, and the prediction model generating unit 122 generates a prediction model that predicts a current value output by the current sensor. In addition, when the diagnostic system 101 includes a plurality of devices 131, the diagnostic processing device 111 can generate a prediction model for each of the devices 131.

[0024] The abnormality determination unit 123 acquires operation data of the device 131, and acquires an actual measurement value of the first sensor based on the operation data. In addition, the abnormality determination unit 123 inputs operation data of a sensor other than the first sensor into a prediction model, and acquires a predicted value of the first sensor.

[0025] Then, the abnormality determination unit 123 determines whether the information on the first physical quantity (information on the value based on the actual measurement value and the predicted value of the first sensor) is abnormal based on the actual measurement value and the predicted value of the first sensor. Note that in this embodiment, it is determined whether the information on the current sensor is abnormal.

[0026] When an abnormality is determined, the contribution value calculation unit 124 performs a factor analysis. Here, the factor analysis is a process of analyzing the influence of other sensors on the abnormality degree of the first sensor in the device 131, and will be described in detail later. In this embodiment, the influence of other sensors on the abnormality degree of the current sensor is analyzed.

[0027] Based on the result of the factor analysis, the abnormal part inferring unit 125 infers an abnormal part in the device 131. The processing of the abnormal part inferring unit 125 will be described in detail later.

[0028] The information output unit 126 generates and outputs data for providing information on an abnormal portion in the device 131. The processing of the information output unit 126 will be described in detail later.

[0029] Next, an example of the overall processing will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of the overall processing in this embodiment. In addition, the subject of the processing is, in detail, the processor 112 of the diagnostic processing device 111.

[0030] The diagnostic processing device 111 acquires the sensor values ​​of each sensor from the sensor unit 132 of the device 131 in the sensor value acquisition unit 121 (S201). The timing of acquiring the sensor values ​​is not particularly limited as long as appropriate processing can be executed. For example, the diagnostic processing device 111 may acquire the sensor values ​​in real time when the device 131 is operating.

[0031] The diagnostic processing device 111 extracts normal data from the sensor value data acquired in S201 in the prediction model generation unit 122 (S202). The diagnostic processing device 111 can extract the normal data based on an appropriate method. For example, the diagnostic processing device 111 may store data indicating normal sensor values ​​of each sensor provided in the device 131 in the storage 114 in advance, and extract data similar to the data stored in the storage 114 from the data acquired in S201. The data stored in advance in the storage 114 may be, for example, data for the first year after delivery of the device 131, in which no abnormality alarm is generated.

[0032] The diagnostic processing device 111 uses the normal data extracted in S202 as teacher data in the prediction model generation unit 122 to generate a prediction model that predicts the sensor value of the first sensor by inputting sensor values ​​of multiple sensors other than the first sensor (S203).

[0033] Here, the first sensor is one of the sensors included in the device 131. Therefore, in S202, normal data of the first sensor and each sensor different from the first sensor are extracted, and in S203, a prediction model that predicts a sensor value of the first sensor is generated. Note that in this embodiment, the first sensor is a current sensor, and a prediction model that predicts a sensor value of the current sensor as a first physical quantity is generated.

[0034] When a predictive model is generated and the diagnostic processing device 111 performs diagnostic processing of the equipment 131, the diagnostic processing device 111, in the abnormality judgment unit 123, acquires the sensor values ​​of each sensor equipped in the equipment 131 that is the target of the diagnostic processing from the sensor value data acquired in S201 as data to be analyzed (S204).

[0035] The diagnostic processing device 111 acquires, in the abnormality determination unit 123, a prediction model for the device 131 to be subjected to the diagnostic processing generated in S203 (S205). Then, the diagnostic processing device 111 inputs a sensor value of a sensor other than the first sensor in the analysis target data acquired in S204 to the prediction model, and acquires an estimated output value of the first sensor for the device 131 to be subjected to the diagnostic processing (S206). In this embodiment, the first sensor is a current sensor, and the diagnostic processing device 111 acquires an estimated output value of the current sensor as a first physical quantity.

[0036] Then, the diagnostic processing device 111 performs an abnormality determination for the device 131 that is the target of the diagnostic processing in the abnormality determination unit 123 (S207). This abnormality determination is performed using an estimated value of the first sensor acquired from the prediction model and an actual measurement value of the first sensor. Here, the actual measurement value of the first sensor (i.e., the actual measurement value of the first physical quantity) is acquired from the analysis target data acquired in S204. In this embodiment, since the first sensor is a current sensor, the diagnostic processing device 111 acquires the actual measurement value of the current sensor as the first physical quantity.

[0037] In this embodiment, the diagnostic processing device 111 determines that the information regarding the first physical quantity is normal when the actual measurement value minus the predicted value (the actual measurement value minus the predicted value) falls within a predetermined range, and determines that the information regarding the first physical quantity is abnormal when the actual measurement value does not fall within the predetermined range.

[0038] For example, the diagnostic processing device 111 uses a value based on a prediction model as a reference point in the state space, and determines that the information on the first physical quantity is normal when an actual measurement value is within a predetermined distance from the reference point along an axis related to the first sensor. On the other hand, the diagnostic processing device 111 determines that the information on the first physical quantity is abnormal when an actual measurement value is not within a predetermined distance from the reference point along an axis related to the first sensor.

[0039] Then, in the diagnostic processing device 111, the abnormality determination unit 123 outputs the result of the abnormality determination made in S207 (S208). In this embodiment, since the first sensor is a current sensor, the determination result based on the value of the current sensor is output.

[0040] If an abnormality is determined, the diagnostic processing device 111 defines the difference between the actual measurement value and the estimated output value of the prediction model as the degree of abnormality in the contribution value calculation unit 124, and evaluates the influence of the other sensor values ​​on the degree of abnormality of the first sensor (S209). If a normality is determined, the process of S209 is omitted.

[0041] In this embodiment, the diagnostic processing device 111 uses SHAP (SHapley Additive exPlanation), an example of an analysis method for a machine learning model, to evaluate the influence of other sensor values ​​on the abnormality degree of the first sensor and calculate the contribution value of each of the other sensors to the abnormality degree of the first sensor.

[0042] For example, in a state space of VQC (Vector Quantization Clustering) or LSC (Local Sub-space Classifier), the degree of anomaly can be calculated from the difference in position coordinates in the state space. For example, in an N-dimensional space in which axes corresponding to the respective sensors of the device 131 are set, the degree of anomaly for the first sensor is defined based on the distance between a reference point along the axis related to the first sensor and a point indicating an actual measurement value. Here, it is not possible to estimate the breakdown of the degree of anomaly (values ​​obtained by allocating the degree of anomaly to each sensor) from the N-dimensional space. On the other hand, it is possible to calculate the breakdown of the degree of anomaly by calculating the SHAP for the degree of anomaly of the first sensor as described above without calculating the SHAP for the first sensor.

[0043] In S209, after evaluating the influence of the other sensor values ​​on the abnormality level of the first sensor, the diagnostic processing device 111 performs a process of estimating an abnormal part in the device 131. First, the diagnostic processing device 111 estimates an abnormal output value by setting a virtual value for the abnormality level of the first sensor for each sensor using the contribution values ​​of the other sensors to the abnormality level of the first sensor in the abnormality part estimation unit 125 (S210). Note that the virtual value may be the contribution value itself, or may be a value representing an influence on the abnormality level based on the contribution value.

[0044] In this embodiment, since the first sensor is a current sensor, a virtual current value for the degree of current abnormality is set as shown in Fig. 3. In the example of Fig. 3, the contribution value of each sensor is found in S209, and a virtual value (virtual current value) using the contribution values ​​of these sensors is set in S210, indicating that the sensor values ​​of sensors A to D among these sensors affect the degree of current abnormality. In other words, it is estimated that the sensor values ​​of sensors A to D are abnormal output values. Here, the virtual current value may be, for example, the contribution value itself for the degree of current abnormality. Also, the virtual current value may be, for example, a value representing an influence on the degree of current abnormality based on the contribution value.

[0045] Then, the diagnostic processing device 111 estimates the abnormal part by performing an analysis based on the virtual value in the abnormal part estimation unit 125. As an example, as shown in Fig. 4, a correspondence table including information on parts of the device 131 and sensors related to the parts is held in the storage 114 of the diagnostic processing device 111. Then, the diagnostic processing device 111 refers to the correspondence table and identifies, as the abnormal part, a part related to a sensor evaluated to be outputting a sensor value that affects the degree of abnormality.

[0046] 3 and 4, if sensor A is a sensor that detects tank pressure, sensor B is a sensor that detects discharge pressure, sensor C is a sensor that detects fan frequency, and part A is a separate element filter, the diagnostic processing device 111 evaluates that the sensor values ​​of the sensors (sensors A to C) affect the degree of abnormality and identifies part A (separate element filter) to which these sensors A to C are linked as the abnormal part. Also, if sensor D is a sensor that detects motor surface temperature, sensor E is a sensor that detects fan frequency, and part B is a bearing unit, the diagnostic processing device 111 evaluates that the sensor value of the sensor (sensor D) affects the degree of abnormality and identifies part B (bearing unit) to which the sensor (sensor D) is linked as the abnormal part.

[0047] In this way, by using a correspondence table (data linking parts of device 131 with each sensor other than the first sensor), it becomes possible to appropriately estimate the abnormal part even when, for example, multiple sensors are associated with one part.

[0048] Furthermore, the diagnostic processing device 111 generates data for indicating the result of the diagnostic processing in the information output unit 126 and outputs it to the information output device 151 (S210). The user can grasp the result of the diagnostic processing using the information output device 151. The diagnostic processing device 111 generates and outputs data for providing information on an abnormal part, for example. Furthermore, the diagnostic processing device 111 generates and outputs data for providing information on the influence of the abnormal part on the degree of abnormality, using the contribution values ​​of the respective sensors related to the abnormal part.

[0049] An example of the information to be provided is shown in Fig. 5. In the example of Fig. 5, the first sensor is a current sensor, and the sensors A to D and the sites A and B are the same as those described above.

[0050] Therefore, in this example, the user can know that the separate element filter (part A) is the abnormal part. The user can also know the extra power consumption (i.e., the abnormal output value) of the separate element filter. The extra power consumption value (9A in FIG. 5) of the separate element filter is a value based on the virtual current value set for each of the sensors A to C linked to the separate element filter using the contribution values ​​of the sensors A to C. The user can also know that the bearing unit (part B) is the abnormal part. The user can also know the extra power consumption (i.e., the abnormal output value) of the bearing unit. The extra power consumption value (2A in FIG. 5) of the bearing unit is a value based on the virtual current value set for each of the sensors D to E linked to the bearing unit using the contribution values ​​of the sensors D to E.

[0051] The information is provided to the user in an appropriate manner. For example, the information output device 151 may display the results of the diagnostic process on a display provided in the information output device 151. The display manner may be the same as that shown in FIG. 5, for example. That is, as shown in FIG. 5, a display including a graph showing the influence of each sensor on the abnormality level, information showing the abnormal part, and a sentence explaining the abnormal output value of the part may be performed. However, as long as the results of the diagnostic process can be appropriately presented, the display manner can be appropriately changed. For example, the influence of the abnormal part (power consumption in the example of FIG. 5) may be displayed as a percentage of the overall abnormality level. For example, the influence of the abnormal part on the overall abnormality level may be displayed as a percentage. In addition, as a result of the diagnostic process, for example, the amount of power consumption (electricity bill) for each part may be displayed. The amount of power consumption can be calculated by a known method using the power consumption value.

[0052] For example, the result of the diagnostic process may be provided by voice. The information output device 151 may output the result of the diagnostic process from a voice output device that the information output device 151 includes.

[0053] Also, for example, the provision of information regarding the influence of each sensor on the degree of abnormality may be omitted, and information regarding the abnormal portion may be provided instead.

[0054] If normality is determined in S208 and S209 is omitted, the virtual value is not set, and the diagnostic processing device 111 generates data for providing a diagnosis result that the device 131 is normal in the information output unit 126, and outputs the data to the information output device 151. Then, the information output device 151 can provide information indicating that the device is normal.

[0055] According to the diagnostic system 101 of this embodiment, the diagnostic processing device 111 monitors the state of the device 131, and when, for example, the first sensor value of the device 131 is abnormal, an evaluation of the degree of abnormality is performed for each part of the device 131, the abnormal part of the device 131 is identified, and a service can be realized that provides information on the abnormal part of the device 131. Then, maintenance is performed based on the provided information, and, for example, improvements are made in energy costs, running costs, and the like. Therefore, the diagnostic system 101 can contribute from an environmental perspective and an economic perspective.

[0056] For example, by evaluating each part for the degree of current anomaly, a part that is consuming more current than normal is identified and a quantitative evaluation of this part is performed. The result is then provided, and maintenance measures are taken based on the provided information, making it possible to use the device 131 with reduced power consumption.

[0057] Furthermore, according to this embodiment, there is no need to create a learning model for each part of the device 131, and the calculation load is reduced.

[0058] Although the embodiments have been described above, the present invention is not limited to the above-described embodiments, and includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the configurations described. In addition, for example, other configurations may be added, deleted, or replaced with respect to part of the configuration of the embodiment.

[0059] In the diagnostic system 101, the number of devices 131 may be one or more. Furthermore, the devices 131 may be the same type or different types.

[0060] The contribution value may be any value that allows an appropriate evaluation of the influence on the abnormality level, and may be, for example, a contribution rate (i.e., a value related to the proportion to the abnormality level).

[0061] In the present embodiment, an example has been described in which an industrial machine is used in the diagnostic system 101. However, the device 131 in the diagnostic system 101 is not limited to an industrial machine. In the diagnostic system 101, for example, a household device (such as a home appliance) may be the device 131 to be diagnosed.

[0062] In the abnormality determination unit 123 in S204 to S207, the diagnostic processing device 111 may perform abnormality determination based on whether the acquired actual measurement value is equal to or greater than a predetermined threshold. Here, the predetermined threshold can be set appropriately. The predetermined threshold may be set based on, for example, an average value of the past abnormality degrees. [Explanation of symbols]

[0063] 101 Diagnostic System 111 Diagnostic processing equipment 131 Equipment 151 Information output device

Claims

1. a prediction model generation step in which a processor generates a prediction model for predicting a first physical quantity measured by a first sensor of a plurality of sensors included in the apparatus, based on data obtained from the plurality of sensors when the apparatus is in a normal operating state; an anomaly determination step in which a processor determines an anomaly in information relating to the first physical quantity, using a predicted value of the first physical quantity predicted using the prediction model and an actual measurement value of the first physical quantity measured by the first sensor; a contribution value calculation step of calculating a contribution value of each sensor other than the first sensor to an abnormality degree of the information when the processor determines that the information is abnormal; an estimation step of estimating an abnormal output value of a portion of the device using the contribution value and estimating an abnormal portion of the device by a processor; A diagnostic method comprising the steps of:

2. The diagnostic method according to claim 1, The processor further includes an output step of outputting data for providing information regarding an abnormality portion in the apparatus estimated in the estimation step. A diagnostic method characterized by:

3. The diagnostic method according to claim 2, The processor outputs data for providing information on the abnormal output value of the abnormal portion, the data being generated using the contribution values ​​of the respective sensors related to the abnormal portion in the output step. A diagnostic method characterized by:

4. The diagnostic method according to claim 1, The processor, in the estimation step, estimates an abnormal part by using data in which a part of the device is associated with a sensor different from the first sensor. A diagnostic method characterized by:

5. The diagnostic method according to claim 4, The processor further includes an output step of outputting data for providing information regarding an abnormality portion in the apparatus estimated in the estimation step. A diagnostic method characterized by:

6. The diagnostic method according to claim 5, The processor outputs data for providing information on the abnormal output value of the abnormal portion, the data being generated using the contribution values ​​of the respective sensors related to the abnormal portion in the output step. A diagnostic method characterized by:

7. A processor; A communication device; Equipped with the processor generates a prediction model for predicting a first physical quantity measured by a first sensor of a plurality of sensors included in the device, based on data obtained from the plurality of sensors in a normal operating state of the device via the communication device; the processor determines an anomaly in information regarding a first physical quantity by using a predicted value of the first physical quantity predicted using the prediction model and an actual measurement value of the first physical quantity measured by the first sensor; When the processor determines that the information is abnormal, it calculates a contribution value to an abnormality degree of the information for each sensor other than the first sensor; The processor uses the contribution values ​​to estimate abnormal output values ​​of the portions of the device and to estimate abnormal portions of the device. A diagnostic processing device comprising:

8. The diagnostic processing device according to claim 7, The processor outputs data for providing information regarding the estimated abnormal portion in the device to an outside via the communication device. A diagnostic processing device comprising:

9. The diagnostic processing device according to claim 8, The processor outputs data, generated using the contribution values ​​of the sensors related to the abnormal portion, for providing information regarding the abnormal output value of the abnormal portion via the communication device. A diagnostic processing device comprising:

10. The diagnostic processing device according to claim 7, The processor estimates an abnormality portion by using data associated with a portion of the device and a sensor different from the first sensor. A diagnostic processing device comprising:

11. The diagnostic processing device according to claim 10, The processor outputs data for providing information regarding the estimated abnormal portion in the device to an outside via the communication device. A diagnostic processing device comprising:

12. The diagnostic processing device according to claim 11, the processor outputs data for providing information on the abnormal output value of the abnormal portion, the data being generated using the contribution values ​​of the sensors related to the abnormal portion, to an external device via the communication device; A diagnostic processing device comprising:

13. A terminal that provides a user with information based on data input from the diagnostic processing device according to claim 8.

14. The diagnostic processing device according to claim 8 ; a terminal for providing a user with information based on data input from the diagnostic processing device; A diagnostic system comprising:

15. A processor; A communication device; Equipped with the processor acquires an actual measurement value of a first physical quantity measured by a first sensor of a plurality of sensors included in the device based on data obtained from the plurality of sensors via the communication device; The processor determines an anomaly in the information relating to the first physical quantity based on whether the acquired actual measurement value is equal to or greater than a predetermined threshold value; and When the processor determines that the information is abnormal, it calculates a contribution value to an abnormality degree of the information for each sensor other than the first sensor; The processor uses the contribution values ​​to estimate abnormal output values ​​of the portions of the device and to estimate abnormal portions of the device. A diagnostic processing device comprising:

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