Diagnosis device and diagnosis method

Abstract

A diagnosis device (100) stores a first database (141) for storing correspondence information in which a combination of the characteristics of waveform data pertaining to a wind power generation device (20) and attribute information pertaining to the wind power generation device (20), and a diagnosis result relating to an abnormality in the wind power generation device (20) and maintenance-type diagnosis information pertaining to the wind power generation device (20), are associated with each other. The diagnosis device (100) identifies the diagnosis information on the basis of an acquisition parameter and acquisition attribute information by using the first database (141), and outputs the diagnosis information to a user terminal (50).

Description

Diagnostic device and diagnostic method

[0001] The present disclosure relates to diagnostic devices and methods.

[0002] For example, Japanese Patent Laid-Open Publication No. 2013-185507 (Patent Document 1) discloses a condition monitoring system for a wind turbine generator. This condition monitoring system includes a vibration sensor that detects vibration values ​​at a target position of the wind turbine generator. The condition monitoring system diagnoses the presence or absence of an abnormality based on the vibration values ​​detected by the vibration sensor. The condition monitoring system then displays the diagnosis results on a display unit of a monitoring terminal.

[0003] JP 2013-185507 A

[0004] Conventional condition monitoring devices for wind turbine generators only perform abnormality diagnosis for each measurement data, which can lead to the problem of not being able to perform highly accurate diagnosis. Furthermore, conventional condition monitoring devices for wind turbine generators only display simple diagnosis results on a display unit. Therefore, a separate technician or other person must devise maintenance methods for the wind turbine generator, which can lead to the problem of taking a lot of time.

[0005] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to achieve at least one of highly accurate diagnosis and highly accurate proposal of maintenance types in diagnosing wind power generation equipment.

[0006] The diagnostic device of the present disclosure includes an interface, a memory, and a processing device. The interface acquires parameters used in diagnosing a wind turbine generator as acquired parameters, and acquires attribute information of the wind turbine generator as acquired attribute information. The memory stores correspondence information in which combinations of the diagnostic parameters of the wind turbine generator and the attribute information of the wind turbine generator are associated with diagnostic information including at least one of a diagnosis result regarding an abnormality in the wind turbine generator and a maintenance type for the wind turbine generator. The processing device uses the correspondence information to identify diagnostic information based on the acquired parameters and the acquired attribute information, and outputs the diagnostic information to an external device.

[0007] A diagnostic method according to the present disclosure includes acquiring parameters used in diagnosing a wind turbine generator as acquired parameters and acquiring attribute information of the wind turbine generator as acquired attribute information, and identifying diagnostic information based on the acquired parameters and the acquired attribute information using correspondence information that associates a combination of the diagnostic parameters of the wind turbine generator and the attribute information of the wind turbine generator with diagnostic information including at least one of a diagnosis result related to an abnormality in the wind turbine generator and a maintenance type for the wind turbine generator, and outputting the diagnostic information to an external device.

[0008] According to the present disclosure, in diagnosing a wind turbine generator, at least one of highly accurate diagnosis and highly accurate proposal of maintenance types can be achieved.

[0009] 1 is a diagram illustrating an example of a configuration of a management system of the present disclosure; FIG. 2 is a diagram illustrating an example of a first database; FIG. 3 is a diagram illustrating an example of a second database; FIG. 4 is a diagram illustrating an example of a third database; FIG. 5 is a functional block diagram of a diagnostic device; FIG. 6 is a diagram illustrating an example of a report; FIG. 7 is a diagram illustrating an example of an input screen for appropriateness information; FIG. 8 is a flowchart illustrating main processing of a diagnostic device; FIG. 9 is a flowchart illustrating report creation processing; FIG. 10 is a flowchart illustrating maintenance confirmation processing; and FIG. 11 is a flowchart illustrating update processing.

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0011] 1 is a diagram illustrating an example of the configuration of a management system 10 according to a first embodiment. The management system 10 of the present disclosure includes M (M is an integer equal to or greater than 1) wind power generation units 45, a diagnostic device 100, a user terminal 50, a maintenance terminal 70, and a network NW. A collection device 30, which will be described later, the diagnostic device 100, the user terminal 50, and the maintenance terminal 70 are capable of communicating with each other via the network NW.

[0012] The wind power generation unit 45 includes a wind power generation device 20, a collection device 30, and N vibration sensors Sn (n=1, . . . , N, where N is an integer equal to or greater than 1).

[0013] Identification information (ID) and attribute information are assigned to each of the M wind turbine generators 20. The identification information is information for identifying the wind turbine generator 20. The attribute information is, for example, information indicating the attributes of the wind turbine generator 20, and in this embodiment, is model information indicating the model of the wind turbine generator 20. The attribute information may include, for example, the manufacturing date.

[0014] The wind turbine generator 20 is a device that receives wind power and generates electricity. The wind turbine generator 20 includes a main bearing, a generator, a gearbox, etc. Each vibration sensor Sn detects vibration values ​​of a diagnosis target portion of the wind turbine generator 20 (for example, the main bearing, the gearbox, and the generator). The vibration values ​​are expressed, for example, by any of the displacement, speed, and acceleration of the predetermined portion. The vibration values ​​detected by the vibration sensors Sn and the sensor IDs of the vibration sensors are associated with each other and output to the collection device 30 as time-series data.

[0015] The time series data collected by the collection device 30 is output to the diagnosis device 100. Furthermore, the collection device 30 outputs the wind power generation device ID and attribute information of the wind power generation device 20 corresponding to the collection device 30 to the diagnosis device 100 in association with the time series data.

[0016] The user terminal 50 is a terminal device owned by a user A. "User A" is typically a person who owns a wind power generation device 20, such as a power generation company. The user terminal 50 is typically a mobile terminal that can be carried by the user A. The user terminal 50 may also be a dedicated computer terminal.

[0017] If the diagnostic device 100 detects an abnormality in the wind turbine generator 20, an operator performs maintenance on the wind turbine generator 20. The maintenance terminal 70 is a terminal operated by a maintenance manager C of the maintenance company. For example, the maintenance terminal 70 is a terminal that receives a request for the maintenance. Specifically, if the diagnostic device 100 determines that maintenance of the wind turbine generator 20 is necessary, it transmits maintenance information for requesting the maintenance to the maintenance terminal 70. The diagnostic device 100 refers to a predetermined database (not shown) or the like for information such as the operator's work plan and the delivery status of repair parts, and creates a construction plan such as a schedule for work and parts procurement. The diagnostic device 100 generates maintenance information based on these processes.

[0018] The display unit of the maintenance terminal 70 displays a request image based on the maintenance information. The request image includes, for example, the address of the wind turbine generator 20 to be maintained, the date and time of maintenance, and the type of maintenance (work content) described below. After checking the request image, the maintenance manager C arranges for a worker to arrive at the address and date and time displayed in the image. The "worker" typically refers to a person who will perform maintenance on the wind turbine generator 20 in response to an abnormality. The worker performs maintenance on the wind turbine generator 20 in accordance with the work plan.

[0019] Diagnostic device 100 identifies the diagnosis result of the abnormality diagnosis, the maintenance type, and the like through calculations described below. Then, diagnostic device 100 transmits a report (report 300 in FIG. 6 ) including the diagnosis result of the abnormality diagnosis and the maintenance type to user terminal 50. User terminal 50 corresponds to the "external device" in the present disclosure. Diagnostic device 100 is a terminal operated by administrator B.

[0020] The user terminal 50 displays the report on the display unit, thereby allowing user A to recognize the diagnosis results of the abnormality diagnosis and the maintenance type. Furthermore, the diagnostic device 100 transmits maintenance information including the abnormal location to the maintenance terminal 70. This allows the maintenance manager C to recognize the abnormal location in advance.

[0021] The diagnostic device 100 has a processing device 102, a memory 104, and a communication interface 106. The communication interface is denoted as "communication I / F" in FIG. 1. The processing device 102 executes various processes and calculations. The components are interconnected by a data bus. The memory 104 includes a read-only memory (ROM) and a random access memory (RAM), etc.

[0022] The processing device 102 is configured with a central processing unit (CPU), a field-programmable gate array (FPGA), a graphics processing unit (GPU), etc. The processing device 102 may be configured with at least one of a CPU, an FPGA, and a GPU. The processing device 102 may also be configured with processing circuitry. The processing device 102 is also referred to as "at least one processor" or "processing circuitry."

[0023] The memory 104 includes a volatile storage area (e.g., a working area) that temporarily stores program code, work memory, etc. when the processing device 102 executes any program. For example, the memory 104 includes a random access memory (RAM) and a read-only memory (ROM).

[0024] The ROM stores programs executed by the processing device 102. The RAM temporarily stores data generated by the execution of the programs in the processing device 102. The RAM can function as a temporary data memory used as a working area.

[0025] The communication interface 106 is configured to communicate with devices external to the diagnostic device 100 (such as the collection device 30, the user terminal 50, and the maintenance terminal 70).

[0026] An input device 108 and a display device 110 are connected to the diagnostic device 100. The input device 108 is configured, for example, with a mouse, a keyboard, etc. The diagnostic manager B can input predetermined information using the input device 108. The display device 110 displays the predetermined information.

[0027] [Database] Next, a database used in this embodiment will be described. A database is also referred to as a table. FIG. 2 is a diagram showing an example of a first database (DB: Date Base). The first database is created in advance based on past diagnostic results by the diagnostic device 100 and simulations using the diagnostic device 100. In the example of FIG. 2, the following diagnostic information is associated with each model information of the wind turbine generator. The diagnostic information includes waveform data characteristics, damage level, lifespan of the diagnosed component, diagnostic results, recommended maintenance type, advice, and appropriateness. In addition, a diagnostic ID is assigned to each piece of diagnostic information. In other words, a diagnostic ID is associated with one piece of diagnostic information. Although not shown in FIG. 2, the first database is configured so that the diagnostic device 100 can also identify the diagnosed component for one piece of diagnostic information.

[0028] The model information A of the wind turbine generator is as described above. The features of the waveform data include a feature quantity B and a damage frequency F. The feature quantity B is calculated by a predetermined calculation. Specifically, the predetermined calculation is a calculation for calculating at least one of the effective value of the waveform data, the peak value of the waveform data, the crest factor of the waveform data, the kurtosis of the waveform data, the skewness of the waveform data, and the OA (Overall) value of the waveform data. The feature quantity B may be calculated using raw waveform data, or may be waveform data obtained by applying a predetermined band-pass filter to the raw waveform data. In the example of FIG. 2, feature quantities B1 to B6, etc. are defined as feature quantity B.

[0029] The damage frequency F is the frequency at which vibrations due to damage occur, which is determined from the internal specifications of the components of the wind turbine generator 20 and the rotation frequency of the mounting shaft. The damage frequency F is the frequency at which energy peaks in the frequency spectrum and corresponds to a damaged component. The frequency spectrum is data obtained by performing, for example, a fast Fourier transform (FFT) on the vibration data detected by the vibration sensor Sn.

[0030] The damage level is information indicating the degree of damage to the diagnosed component. The lifespan is information indicating the length of the lifespan of the diagnosed component. The lifespan is the number of blade rotations and time that the diagnosed component can continue to operate. The diagnosis result is information indicating the result of a diagnosis regarding an abnormality in the wind turbine generator 20. The maintenance type is information indicating the type of maintenance recommended for the diagnosed component. The advice is information indicating the content of advice to be notified to the user when continuing to operate the wind turbine generator 20. The appropriateness is information indicating the degree (level) of appropriateness of the diagnostic information (diagnosis result and maintenance type). The technical significance of the appropriateness will be described later.

[0031] The "wind turbine generator model information" in FIG. 2 corresponds to the "wind turbine generator attribute information" of the present disclosure. The "waveform data characteristics" in FIG. 2 correspond to the "wind turbine generator diagnostic parameters" of the present disclosure. The "diagnosis results," "maintenance type," and "advice" in FIG. 2 correspond to the "diagnosis information" of the present disclosure. The first database in FIG. 2 corresponds to the "correspondence information" of the present disclosure. In this way, the first database in FIG. 2 is information in which "combinations of wind turbine generator attribute information and wind turbine generator diagnostic parameters" are associated with "diagnosis results and maintenance types of the wind turbine generator 20." From a different perspective, the first database associates search condition groups with diagnostic information groups. The search condition groups include "combinations of wind turbine generator attribute information, wind turbine generator diagnostic parameters, damage levels, and life spans." The diagnostic information groups include "diagnosis results and maintenance types of the wind turbine generator 20." The diagnostic information groups are also search results of searches based on search conditions.

[0032] Fig. 3 is a diagram showing an example of the second database. In the second database, wind power generation device IDs are associated with user terminal IDs. For example, wind power generation device ID: W1 is associated with user terminal ID: U1. Fig. 4 is a diagram showing an example of the third database. In the third database, maintenance IDs are associated with estimated costs. For example, maintenance ID: M1 is associated with estimated cost: N1.

[0033] [Functional Block Diagram of Diagnostic Device] Fig. 5 is a functional block diagram of diagnostic device 100. Diagnostic device 100 has a receiving unit 112, a processing unit 114, a transmitting unit 116, and a storage unit 118. Receiving unit 112 and transmitting unit 116 correspond to communication interface 106 in Fig. 1. Processing unit 114 corresponds to processing device 102 in Fig. 1. Storage unit 118 corresponds to memory 104 in Fig. 1, and at least a portion of the storage area of ​​memory 104 is applied to storage unit 118.

[0034] The memory unit 118 stores a first database 141 shown in FIG. 2, a second database 142 shown in FIG. 3, a third database 143 shown in FIG. 4, a calculation formula 144 (formula (1) described below), and the like.

[0035] The receiving unit 112 acquires vibration data, which is time-series data, model information of the wind turbine generator 20, and the wind turbine generator ID of the wind turbine generator 20 from the M collection devices 30. The vibration data corresponds to the "acquired parameters" in the present disclosure. The vibration data is a parameter used to diagnose the wind turbine generator 20. Furthermore, the model information corresponds to the "acquired attribute information" in the present disclosure.

[0036] The processing unit 114 detects the characteristics of the waveform data (presence or absence of a characteristic amount and a damage frequency peak) by performing the above-mentioned predetermined calculations on the vibration data (acquired parameters). Furthermore, the processing unit 114 estimates the damage level and the lifespan of the diagnosed part by a predetermined estimation process based on the characteristics.

[0037] The processing unit 114 then refers to the search conditions (first database 141) using the model information, waveform data characteristics, damage level, and lifespan as keys to identify diagnostic information. That is, the processing unit 114 refers to the search conditions (first database 141) based on the vibration data (acquired parameters) and model information (acquired attribute information) to identify diagnostic information. The processing unit 114 then creates a report including the identified diagnostic information. The processing unit 114 also refers to the third database 143 to create the report.

[0038] The processing unit 114 also uses the wind turbine generator ID to refer to the second database 142 to identify the user terminal ID of the destination user terminal 50. The processing unit 114 then transmits the report via the transmission unit 116 to the user terminal 50 indicated by the identified user terminal ID.

[0039] Furthermore, if the result of the abnormality diagnosis of the wind turbine generator 20 indicates that maintenance is necessary, the processing unit 114 transmits the above-mentioned maintenance information to the maintenance terminal 70 .

[0040] 6 is a diagram showing an example of a report 300 created by the processing unit 114. The report 300 includes the diagnosis results (diagnosis information) by the processing unit 114. The report 300 is displayed as a report image on the user terminal 50. Specifically, the report 300 includes diagnosis result information 301, maintenance type information 302, estimated amount information 303, cost-effectiveness information 304, and maintenance confirmation information 305.

[0041] The diagnosis result information 301 is information indicating the result of the diagnosis using the first database 141. In the example of Fig. 6, the diagnosis result information 301 is information indicating that the diagnosis result is "moderate damage to the bearing device."

[0042] The maintenance type information 302 is information indicating the maintenance type. In the example of Fig. 6, the maintenance type information 302 is information indicating that the maintenance type is "inspection C3".

[0043] The estimate information 303 is information indicating the estimated cost of maintenance of the maintenance type indicated in the maintenance type information 302. In the example of Fig. 6, the estimate information 303 is information indicating that the estimated maintenance cost is "X1 yen."

[0044] The cost-effectiveness information 304 is information indicating the cost-effectiveness of performing maintenance of the maintenance type indicated in the maintenance type information 302. For example, the diagnostic device 100 uses the evaluation result of the lifespan of the wind power generation device 20 to calculate the effect of reducing maintenance costs (the effect of reducing downtime by making advance arrangements) when maintenance of the wind power generation device 20 is immediately ordered. This calculation is performed using, for example, a predetermined database and calculation formula. The cost-effectiveness information 304 in the example of FIG. 6 is information indicating that the cost-effectiveness amount is "X2 yen".

[0045] The maintenance confirmation information 305 is information for receiving from the user whether or not to perform maintenance. In the example of Fig. 6, the maintenance confirmation information 305 includes the phrase "Do you want to perform maintenance?", a YES button, and a NO button.

[0046] The user checks the contents of the report 300, and if the user desires the maintenance indicated in the maintenance type information 302, he operates the YES button, and if the user does not desire the maintenance, he operates the NO button.

[0047] When the user operates the YES button, user terminal 50 transmits a YES signal indicating that the YES button has been operated to diagnostic device 100. Upon receiving the YES signal, diagnostic device 100 transmits maintenance information based on the contents of report 300 to maintenance terminal 70.

[0048] On the other hand, when the user operates the NO button, the user terminal 50 transmits a NO signal indicating that the NO button has been operated to the diagnostic device 100. When the diagnostic device 100 receives the NO signal, it does not transmit the maintenance information to the maintenance terminal 70.

[0049] [Appropriateness Information] Next, the appropriateness information will be described. The user can input appropriateness information regarding the appropriateness (usefulness) of the report in FIG. 6 and the performed maintenance indicated in the maintenance type information 302 of the report into the user terminal 50. The input appropriateness information is transmitted to the diagnostic device 100. The diagnostic device 100 updates the appropriateness (see FIG. 2) corresponding to the identified diagnostic information based on the appropriateness information input by the user.

[0050] First, the input screen for appropriateness information will be described. Fig. 7 is a diagram showing an example of an input screen 400 for appropriateness information. Diagnostic device 100 displays input screen 400 of Fig. 7 on user terminal 50, for example, a certain period of time (e.g., one month) after the report is transmitted. In other words, input screen 400 is a questionnaire input screen for the report of Fig. 6 and the performed maintenance.

[0051] 7, input screen 400 includes first information 401 and second information 402. Second information 402 further includes information 411, information 412, information 413, and information 414.

[0052] The first information 401, information 411, information 412, information 413, and information 414 are information related to questions for the user. Therefore, the first information 401, information 411, information 412, information 413, and information 414 are also referred to as the first question, the second question, the third question, the fourth question, and the fifth question, respectively (see step S304 and step S306 in FIG. 11 ). A YES button and a NO button are displayed for each of the first information 401, information 411, information 412, information 413, and information 414. The user then operates the YES button or the NO button for all of the first information 401, information 411, information 412, information 413, and information 414. The user then asks the maintenance worker about any points about the first information 401, information 411, information 412, information 413, and information 414 that the user does not understand.

[0053] The first information 401 indicates whether or not the maintenance of the maintenance type recommended by the report in Fig. 6 was performed. In Fig. 7, the first information 401 is a wording image that reads, "Was the maintenance possible?" For example, if the maintenance was performed on the wind turbine generator 20 (the maintenance was possible), the user operates the YES button. On the other hand, if the maintenance was not performed on the wind turbine generator 20 (the maintenance was not possible) due to a structural problem or the like of the wind turbine generator 20, the user operates the NO button.

[0054] The second information 402 is information indicating the satisfaction level of the user A with respect to the report (diagnostic information). As described above, the second information 402 includes information 411, information 412, information 413, and information 414.

[0055] Information 411 is information indicating whether or not an abnormality has been found by the worker in the wind turbine generator 20. In Fig. 7, information 411 is a wording image that reads, "Have you found any abnormalities in the wind turbine generator 20?" For example, if an abnormality has been found in the location indicated by the diagnosis result information 301 in the report in Fig. 6, the user operates the YES button. On the other hand, if no abnormality has been found in the wind turbine generator 20, the user operates the NO button.

[0056] Information 412 is information indicating whether or not there has been an improvement due to maintenance of the wind turbine generator 20. In FIG. 7 , information 412 is an image of the words "Has the wind turbine generator improved as a result of maintenance?" An improvement of the wind turbine generator 20 due to maintenance means, for example, an increase in the amount of power generated by the wind turbine generator 20 compared to before the maintenance. If the wind turbine generator 20 has improved, the user operates the YES button. On the other hand, if the wind turbine generator 20 has not improved, the user operates the NO button.

[0057] Information 413 is information relating to the appropriateness of the degree of abnormality of the wind turbine generator. In Fig. 7, information 413 is a text image that reads "Was the damage level appropriate?" For example, the user inquires about the damage level from the worker who performed the maintenance, and if the damage level in the diagnosis result information 301 shown in the report in Fig. 6 is appropriate, the user operates the YES button. On the other hand, if the damage level is not appropriate, the user operates the NO button.

[0058] Information 414 is information relating to the appropriateness of replacing parts of the wind turbine generator 20. When the diagnostic device 100 identifies part replacement maintenance (such as replacement of bearings and gearboxes in FIG. 2 ) as the maintenance type, the diagnostic device 100 displays the information 414 on the user terminal 50. If part replacement maintenance is appropriate, the user operates the YES button. On the other hand, if part replacement maintenance is not appropriate, the user operates the NO button.

[0059] As is clear from the questions in information 411 to 414, pressing the YES button indicates that the customer is satisfied with the report or maintenance content, whereas pressing the NO button indicates that the customer is not satisfied with the report or maintenance content.

[0060] In this way, a user can input appropriateness information indicating the user's appropriateness for the diagnostic result. Furthermore, multiple users may input appropriateness information for one piece of diagnostic information (diagnostic result and maintenance type). Hereinafter, the number of appropriateness information input for one piece of diagnostic information is also referred to as the "number of records."

[0061] Next, updating of the appropriateness information will be described. The user terminal 50 transmits the appropriateness information input to the user terminal 50 to the diagnostic device 100. The processing unit 114 of the diagnostic device 100 calculates the appropriateness using the appropriateness information and predetermined update information. The processing unit 114 then updates the stored appropriateness E to the calculated appropriateness.

[0062] The update information is, for example, a predetermined calculation formula 144 (function) shown in FIG. 5 . More specifically, calculation formula 144 is a formula that updates the appropriateness so that the appropriateness becomes an extremely low value when first information 401 indicates that maintenance has not been performed. In the present disclosure, an "extremely low value" is defined as the minimum value of the appropriateness (e.g., "0"). Calculation formula 144 is a formula that updates the appropriateness so that the appropriateness becomes large when second information 402 indicates that satisfaction is high. For example, calculation formula 144 is expressed by the following formula (1):

[0063] Appropriateness E=a×((b+c+d+e) / N) (1) Here, as described above, the minimum value of appropriateness E is "0." Furthermore, "a" in formula (1) is a value that becomes "1" when the YES button in the first information 401 is operated, and becomes "0" when the NO button in the first information 401 is operated.

[0064] Furthermore, N in formula (1) is the number of all records of one piece of diagnostic information corresponding to the calculated appropriateness E. "b", "c", "d", and "e" in formula (1) are the numbers of records that are operated as YES in information 411, information 412, information 413, and information 414, respectively.

[0065] [Flowchart] Fig. 8 is a flowchart showing the main processing of the diagnosis device 100 of this embodiment. Note that the three dots in Fig. 8 indicate that a certain period of time has elapsed. Furthermore, the processing in Fig. 8 is executed at predetermined intervals. The predetermined period is, for example, a period for accumulating time-series data required to diagnose an abnormality in the wind turbine generator 20.

[0066] In step S2, the diagnostic device 100 executes a report creation process. The report creation process is mainly a process for creating the report of FIG. 6. Next, in step S4, the diagnostic device 100 executes a maintenance confirmation process. The maintenance confirmation process is mainly a process for confirming whether or not maintenance will be performed. Next, in step S6, the diagnostic device 100 executes an update process. The update process is mainly a process for updating the first database of FIG. 2.

[0067] 9 is a flowchart showing the report creation process in step S2. First, in step S102, the diagnostic device 100 acquires attribute information (acquired attribute information) of the wind turbine generator 20, vibration data (acquired parameters) for a predetermined period (accumulation period), and the wind turbine generator ID from the collection device 30. The vibration data for the predetermined period is the time-series data described above.

[0068] Next, in step S104, the diagnostic device 100 calculates the feature amount by executing the above-mentioned predetermined calculation from the time-series data.

[0069] Next, in step S106, the diagnostic device 100 determines whether the feature amount exceeds a threshold value. If the determination in step S106 is NO, there is no abnormality in the wind turbine generator 20 to be diagnosed, and the process in Fig. 9 ends. On the other hand, if the determination in step S106 is YES, the process proceeds to step S108.

[0070] In step S108, the diagnostic device 100 executes a peak determination process. Specifically, the diagnostic device 100 generates a frequency spectrum by performing a fast Fourier transform on the time-series data. Then, the diagnostic device 100 calculates the peak energy of the damage frequency of each component using a band-pass filter. When the peak energy exceeds a second threshold, the diagnostic device 100 determines that there is a peak that matches the damage frequency (i.e., there is a damaged component). Note that the damage frequency may include harmonic components.

[0071] In step S110, the diagnostic device 100 executes an estimation process. As described above, the estimation process is a process for estimating the damage level and the lifespan of the diagnostic target component based on the characteristics of the waveform data (feature amounts and the presence or absence of a damage frequency peak). The estimation process is executed, for example, by a predetermined calculation.

[0072] Next, in step S112, the diagnostic device 100 temporarily stores search conditions in, for example, the RAM. The search conditions include model information of the wind turbine generator 20, characteristics of the waveform data (feature amounts exceeding the first threshold and the presence or absence of peaks determined in step S108), and the estimation results of step S110 (damage level and lifespan).

[0073] Next, in step S114, a specifying process is executed to specify diagnostic information in the first database (see FIG. 2) using the search criteria stored in step S112. This specifying process is also a "search process" for searching for diagnostic information.

[0074] Here, in the process of identifying diagnostic information (process of searching for diagnostic information), it is determined whether or not there is a search condition that matches the search condition of step S112 from the search condition group of FIG. 2. If the search condition group of FIG. 2 contains a search condition that completely matches the search condition of step S112, the number of diagnostic information candidates (number of hits) is the number of completely matching search conditions. If the search condition group of FIG. 2 contains a search condition similar to the search condition of step S112, the number of diagnostic information candidates is the number of similar search conditions. If the search condition group of FIG. 2 does not contain a search condition that is identical or similar to the search condition of step S112, the number of diagnostic information candidates is "0."

[0075] In step S116, diagnostic device 100 determines whether the number of diagnostic information candidates is two or more. If the number of candidates is two or more (YES in step S116), diagnostic device 100 identifies diagnostic information from the diagnostic information candidates based on the appropriateness associated with the candidate diagnostic information in step S118. Specifically, in step S118, diagnostic device 100 identifies the diagnostic information with the highest appropriateness E (see FIG. 2 ) from the two or more candidate diagnostic information. Then, the process proceeds to step S130.

[0076] Furthermore, in step S116, if the number of diagnostic information candidates is not two or more (NO in step S116), in step S120, diagnostic device 100 determines whether the number of diagnostic information candidates is 1. If the number of diagnostic information candidates is 1 (YES in step S120), diagnostic device 100 identifies the one candidate diagnostic information in step S122.

[0077] Furthermore, in step S120, if the number of candidates for diagnostic information is not 1 (NO in step S120), the process proceeds to step S124. If the number of candidates for diagnostic information is not 1, that is, if the number of candidates for diagnostic information is "0."

[0078] In step S124, diagnostic device 100 determines whether the search criteria include model information. Since the search criteria include model information in the first determination of step S124, a YES determination is made in step S124. Next, in step S128, the model information is excluded (deleted) from the search criteria, and a search for diagnostic information is performed again.

[0079] Then, diagnostic device 100 executes the processes of steps S116 and S120. Furthermore, in step S124 after a NO determination is made in step S116 and a NO determination is made in step S120 (the second determination in step S124), the search criteria do not include model information, so a NO determination is made in step S124. In this case, no matter how many times the search process is performed, the number of diagnostic information candidates will never exceed one. Therefore, in step S126, diagnostic device 100 allows administrator B (engineer) to input diagnostic information. In step S126, diagnostic device 100 displays a text image saying "Please input diagnostic information" on display device 110 (see FIG. 1). Administrator B inputs diagnostic information to display device 110 using input device 108.

[0080] In step S130, a maintenance estimate and cost-effectiveness are calculated based on any one of the diagnostic information identified in step S118, the diagnostic information identified in step S122, and the diagnostic information input in step S126. Next, in step S132, diagnostic device 100 generates report 300 of FIG. 6 and transmits it to user terminal 50. This report 300 includes any one of the diagnostic information identified in step S118, the diagnostic information identified in step S122, and the diagnostic information input in step S126, as well as the maintenance estimate and cost-effectiveness calculated in step S130. Then, the report creation process ends.

[0081] 10 is a flowchart of the maintenance confirmation process in step S4. The maintenance confirmation process is executed when the YES button or NO button in the maintenance confirmation information 305 of the report 300 output in step S132 of FIG.

[0082] In step S204, diagnostic device 100 determines whether maintenance should be performed. In step S204, if the YES button in maintenance confirmation information 305 is operated, a YES determination is made, and if the NO button is operated, a NO determination is made.

[0083] If the determination in step S204 is YES, then in step S206 diagnostic device 100 transmits the maintenance information (see FIG. 5) to maintenance terminal 70. If the determination in step S204 is NO or if the processing in step S206 is completed, the maintenance confirmation processing ends.

[0084] 11 is a flowchart of the update process of step S6. The update process is executed when user A transmits the appropriateness information of FIG.

[0085] In step S302, diagnostic device 100 acquires appropriateness information input by the user. Next, in step S304, diagnostic device 100 determines whether the answer to the first question (first information 401) is YES.

[0086] If the determination in step S304 is NO, it means that a maintenance type that cannot be performed has been recommended to the user. In this case, the user feels uncomfortable. Therefore, in step S308, the process of identifying (searching for) diagnostic information is executed again.

[0087] In step S308, diagnostic information associated with the next highest appropriateness level after the appropriateness level of the diagnostic information most recently identified in step S118 or step S122 is identified. Then, the process returns to step S130 in Fig. 9. In step S130, diagnostic device 100 executes the process of step S132 based on the diagnostic information identified in step S308.

[0088] If step S304 returns a YES answer, diagnostic device 100 proceeds to step S306 to determine whether the second through fifth questions (information 411 through information 414 in FIG. 7) are all NO. If step S306 returns a YES answer, this means that the user's satisfaction with the diagnostic information is extremely low. In this case, the user feels uncomfortable. Therefore, diagnostic device 100 proceeds to step S308.

[0089] If it is determined as NO in step S306, the process proceeds to step S310. If it is determined as NO in step S306, this corresponds to "the case where the appropriateness information satisfies a predetermined standard" in the present disclosure.

[0090] In step S310, diagnostic device 100 transmits the advice (see FIG. 2) included in the identified diagnostic information to user terminal 50. Then, in step S312, the combination of the search criteria stored in step S112 and the identified diagnostic information is additionally stored in the first database. The appropriateness corresponding to this combination is set to a predetermined value. Furthermore, in step S312, diagnostic device 100 updates the appropriateness corresponding to the identified diagnostic information using the above formula (1).

[0091] [Summary] (1) The diagnostic device 100 of this embodiment acquires, as acquired parameters, parameters used for diagnosing the wind turbine generator. At the same time, the diagnostic device 100 acquires, as acquired attribute information, attribute information of the wind turbine generator 20. The diagnostic device 100 has a memory 104 that stores a first database 141. The first database 141 associates combinations of diagnostic parameters of the wind turbine generator 20 and attribute information of the wind turbine generator 20 with diagnostic information including a diagnosis result related to an abnormality in the wind turbine generator 20 and a maintenance type for the wind turbine generator 20. The processing device (processing unit 114) identifies diagnostic information based on the acquired parameters and attribute information using the first database 141, and outputs the diagnostic information to the user terminal 50.

[0092] For example, if a diagnosis is performed using only the diagnostic parameters of the wind power generator 20 without using the attribute information of the wind power generator 20, as in conventional diagnostic devices, the accuracy of the diagnostic results may be reduced due to, for example, an excessively large number of diagnostic result candidates. In contrast, the diagnostic device 100 of the present embodiment uses past diagnostic results (first database) to identify diagnostic results based on not only the diagnostic parameters of the wind power generator 20 but also the attribute information of the wind power generator 20. Therefore, the diagnostic device 100 can automatically and accurately diagnose damage that frequently occurs in wind power generators with the same attributes. Therefore, compared to a diagnosis using only the diagnostic parameters of the wind power generator 20 without using the attribute information of the wind power generator 20, the diagnostic device 100 of the present embodiment can perform a more accurate diagnosis. Furthermore, the diagnostic device 100 of the present embodiment also identifies recommended maintenance based on past maintenance cases. Depending on the wind power generator, there may be maintenance that cannot be performed due to the structure of the equipment. By utilizing past data accumulated in the database, it is possible to automatically determine the appropriate maintenance for the same type of wind power generator. Therefore, since there is no need for engineers or the like to devise maintenance for the wind turbine generator, maintenance services can be provided quickly, and the problem of time-consuming maintenance can be avoided.

[0093] (2) Diagnostic device 100 has first database 141 created in advance, and executes a specification process for specifying candidates for diagnostic information by referring to first database 141. Diagnostic device 100 then specifies diagnostic information from the candidates for diagnostic information.

[0094] According to this configuration, it is possible to identify candidates for diagnostic information by using the first database 141 created in advance, and to identify the diagnostic information.

[0095] (3) The diagnostic device 100 executes an estimation process (step S110 in FIG. 9 ) to estimate at least one of the lifespan and the damage level of a target component of the wind power generation device 20. Then, the diagnostic device 100 executes a specification process (step S114 in FIG. 9 ) by referring to the first database 141 using the estimation result of the estimation process as a search condition.

[0096] According to this configuration, the diagnostic information is identified by combining the estimation result of the current estimation process with a database created in the past, thereby improving the accuracy of the diagnostic information.

[0097] 2, in first database 141, each of a plurality of diagnostic information items is associated with an appropriateness level E indicating the appropriateness of the diagnostic information item. When the number of diagnostic information items is two or more (YES in step S116), diagnostic device 100 identifies diagnostic information items from the candidate diagnostic information items based on the appropriateness levels associated with the candidate diagnostic information items (step S118).

[0098] With this configuration, when the number of candidates for diagnostic information is two or more, the diagnostic information can be narrowed down based on the appropriateness associated with the candidate diagnostic information, thereby improving the accuracy of the diagnostic information.

[0099] (5) After outputting the diagnostic information (report 300 in FIG. 6 ) to an external device (user terminal 50), diagnostic device 100 allows the user terminal to receive appropriateness information (see FIG. 7 ) regarding the appropriateness of the diagnostic information. Diagnostic device 100 updates the appropriateness based on the appropriateness information (step S312 in FIG. 11 ).

[0100] According to this configuration, the user's intention can be reflected appropriately, and therefore the accuracy of the diagnostic information can be improved while improving the convenience for the user.

[0101] (6) When the number of candidates for diagnostic information is two or more, diagnostic device 100 identifies the diagnostic information with the highest appropriateness from the two or more candidates (step S118). As shown in FIG. 7 , the appropriateness information includes first information 401 indicating whether or not maintenance for the maintenance type has been performed. When first information 401 indicates that maintenance has not been performed, diagnostic device 100 updates the appropriateness to the minimum value (see calculation formula 144, which is formula (1) above).

[0102] With this configuration, it is possible to prevent the type of maintenance that could not be performed from being output to an external device.

[0103] (7) As shown in Fig. 7 , the appropriateness information includes second information 402 indicating the degree of satisfaction of the user of the wind power generation device 20 with the report 300. When the second information indicates high satisfaction, the diagnostic device 100 updates the appropriateness to increase it (see calculation formula 144, which is the above formula (1)).

[0104] With this configuration, diagnostic information that provides high user satisfaction can be more easily output to an external device.

[0105] (8) As shown in Fig. 7 , the second information 402 includes information 411, information 412, information 413, and information 414. Information 411 is information indicating whether an abnormality has been found in the wind power generation device 20. Information 412 is information indicating whether an improvement has been made through maintenance of the wind power generation device 20. Information 413 is information regarding the appropriateness of the degree of abnormality in the wind power generation device. Information 414 is information regarding the appropriateness of replacing parts of the wind power generation device 20.

[0106] With this configuration, the information 411, the information 412, the information 413, and the information 414 can be reflected appropriately.

[0107] (9) If the appropriateness information satisfies the predetermined standard (NO in step S306), the diagnostic device 100 outputs advice information regarding the operation of the wind power generation device 20 to the user terminal 50 (step S310).

[0108] With this configuration, the user can be made aware of advice information regarding the operation of the wind turbine generator 20 .

[0109] (10) If the appropriateness information does not satisfy the predetermined criteria (NO in step S304 or YES in step S306), the diagnostic device 100 executes the identification process again based on the appropriateness of the diagnostic information excluding the identified diagnostic information from among the multiple diagnostic information (step S308).

[0110] With this configuration, if the appropriateness information does not satisfy the predetermined criteria, the identification process for identifying diagnostic information is executed again. In other words, diagnostic device 100 can search for similar cases in other models and output diagnostic information whose appropriateness information satisfies the predetermined criteria to an external device.

[0111] (11) Diagnostic device 100 repeats the identification process until the appropriateness information satisfies the predetermined standard (repeating the processes of steps S130, S132, S204, S206, S302, S304, S306, and S308 until a negative determination is made in step S306).

[0112] According to this configuration, it is possible to output to an external device diagnostic information whose appropriateness information satisfies a predetermined standard.

[0113] (12) When the number of candidates for diagnostic information is one (YES in step S120), diagnostic device 100 outputs the candidate diagnostic information (step S122).

[0114] With this configuration, appropriate diagnostic information can be output to the external device. (13) If the number of candidates for diagnostic information is 0 (NO in step S120), diagnostic device 100 executes a new identification process based on the acquired parameters without using the model information (acquired attribute information) (step S128).

[0115] According to this configuration, even if the number of candidates for diagnostic information is zero, a new specific process is executed without using the acquired attribute information, so that the number of candidates for diagnostic information can be set to one or more.

[0116] (14) When the number of diagnostic information candidates is zero even after a new identification process is executed (when the process of step S128 is executed but the result of step S120 is NO), diagnostic device 100 allows input of new diagnostic information (step S126). Then, diagnostic device 100 outputs this new diagnostic information to user terminal 50.

[0117] According to this configuration, if the number of candidates for diagnostic information is zero even after a new identification process is executed, new diagnostic information input by an administrator or the like can be output to an external device.

[0118] (15) The diagnostic information (report 300) includes an estimated cost of maintenance by maintenance type (estimated cost information 303).

[0119] With this configuration, the estimated cost of maintenance can be output to an external device. (16) The diagnostic information (report 300) includes the cost-effectiveness amount (cost-effectiveness information 304) of performing maintenance according to the maintenance type.

[0120] With this configuration, the cost-effectiveness amount can be output to an external device. The diagnostic device 100 outputs maintenance information indicating that maintenance will be performed to the maintenance terminal 70 of the worker who will perform maintenance on the wind turbine generator (step S206 in FIG. 10).

[0121] According to this configuration, it is possible to have a worker or the like perform maintenance on the wind turbine generator 20 without requiring a user to perform maintenance ordering processing.

[0122] [Modifications] (1) In the above embodiment, the sensor used to detect the presence or absence of an abnormality in the wind turbine generator 20 is a vibration sensor. However, the sensor may be another sensor. Examples of the other sensor include a temperature sensor, an AE (Acoustic Emission) sensor, a displacement sensor, or a sound sensor.

[0123] (2) In the above embodiment, the correspondence information is described as the first database 141. However, the correspondence information may be other information. For example, the correspondence information may be a function that outputs a diagnosis result and a maintenance type when model information of the wind turbine generator 20 and characteristics of the waveform data are input.

[0124] [Note] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the description of the above embodiments, and is intended to include all modifications within the meaning and scope of the claims.

[0125] (Supplementary Note 1) A diagnostic device comprising: an interface that acquires parameters used in diagnosing a wind power generation device as acquired parameters and acquires attribute information of the wind power generation device as acquired attribute information; a memory that stores correspondence information in which combinations of the diagnostic parameters of the wind power generation device and the attribute information of the wind power generation device are associated with diagnostic information including at least one of a diagnosis result regarding an abnormality in the wind power generation device and a maintenance type of the wind power generation device; and a processing device that uses the correspondence information to identify diagnostic information based on the acquired parameters and the acquired attribute information and outputs the diagnostic information to an external device.

[0126] According to this configuration, not only the diagnostic parameters of the wind turbine generator but also the attribute information of the wind turbine generator are used to identify diagnostic information including at least one of a diagnostic result regarding an abnormality in the wind turbine generator and a maintenance type for the wind turbine generator, thereby improving the accuracy of at least one of the diagnostic result and the maintenance type.

[0127] (Appendix 2) The correspondence information is a database in which multiple combinations are associated with multiple pieces of diagnostic information corresponding to each of the multiple combinations, and the processing device executes a specification process to identify candidates for diagnostic information by referring to the database, and specifies diagnostic information from the candidates for diagnostic information, in the diagnostic device described in Appendix 1.

[0128] According to this configuration, it is possible to identify candidates for diagnostic information by using a database created in advance, and to identify the diagnostic information.

[0129] (Supplementary Note 3) The diagnostic device according to Supplementary Note 2, wherein the processing device executes an estimation process to estimate at least one of the lifespan of a target component of the wind power generation device and the damage level of the target component, and executes a specific process by referring to a database using the estimation result of the estimation process as a search condition.

[0130] According to this configuration, the diagnostic information is identified by combining the estimation result of the current estimation process with a database created in the past, thereby improving the accuracy of the diagnostic information.

[0131] (Appendix 4) A diagnostic device as described in Appendix 2 or Appendix 3, in which, in the database, each of multiple diagnostic information is associated with an appropriateness level indicating the appropriateness of the diagnostic information, and the processing device identifies diagnostic information from the candidate diagnostic information based on the appropriateness level associated with the candidate diagnostic information when the number of candidate diagnostic information is two or more.

[0132] With this configuration, when the number of candidates for diagnostic information is two or more, the diagnostic information can be narrowed down based on the appropriateness associated with the candidate diagnostic information, thereby improving the accuracy of the diagnostic information.

[0133] (Supplementary Note 5) The diagnostic device according to Supplementary Note 4, wherein the processing device outputs the diagnostic information to an external device, and then allows the user terminal to receive appropriateness information regarding the appropriateness of the diagnostic information, and updates the appropriateness based on the appropriateness information.

[0134] According to this configuration, the user's opinion can be reflected appropriately, and therefore the accuracy of the diagnostic information can be improved while improving the convenience for the user.

[0135] (Supplementary Note 6) A diagnostic device according to Supplementary Note 5, wherein the processing device, when the number of candidates for diagnostic information is two or more, identifies the diagnostic information with the highest appropriateness from the two or more candidates, the diagnostic information including the diagnostic result and the maintenance type, the appropriateness information including first information indicating whether maintenance of the maintenance type has been performed, and when the first information indicates that maintenance has not been performed, the processing device updates the appropriateness to the minimum value.

[0136] With this configuration, it is possible to prevent the type of maintenance that could not be performed from being output to an external device.

[0137] (Appendix 7) A diagnostic device as described in Appendix 6, wherein the appropriateness information includes second information indicating the satisfaction level of the user of the wind power generation device with the diagnostic information, and the processing device updates the appropriateness to be higher when the second information indicates high satisfaction.

[0138] With this configuration, diagnostic information that provides high user satisfaction can be more easily output to an external device.

[0139] (Appendix 8) The diagnostic device described in Appendix 7, wherein the second information includes at least one of information indicating whether an abnormality has been found in the wind power generation equipment, information indicating whether or not an improvement has been made through maintenance of the wind power generation equipment, information regarding the appropriateness of the degree of abnormality in the wind power generation equipment, and information regarding the appropriateness of replacing parts of the wind power generation equipment.

[0140] With this configuration, at least one of these four pieces of information can be reflected appropriately.

[0141] (Supplementary Note 9) The diagnostic device according to any one of Supplementary Note 5 to Supplementary Note 8, wherein the processing device outputs advice information relating to operation of the wind power generation device to an external device when the suitability information satisfies a predetermined standard.

[0142] With this configuration, the user can be made aware of advice information relating to the operation of the wind turbine generator.

[0143] (Supplementary Note 10) The diagnostic device according to Supplementary Note 9, wherein, when the appropriateness information does not satisfy a predetermined standard, the processing device executes the identification process again based on the appropriateness from diagnostic information excluding the identified diagnostic information from among the multiple diagnostic information.

[0144] With this configuration, if the appropriateness information does not satisfy the specified criteria, the identification process for identifying diagnostic information is executed again, so that diagnostic information whose appropriateness information satisfies the specified criteria can be output to an external device.

[0145] (Supplementary Note 11) The diagnostic device according to Supplementary Note 10, wherein the processing device repeats the specific processing until the appropriateness information satisfies a predetermined standard.

[0146] According to this configuration, it is possible to output to an external device diagnostic information whose appropriateness information satisfies a predetermined standard.

[0147] (Supplementary Note 12) The diagnostic device according to any one of Supplementary Note 2 to Supplementary Note 11, wherein the processing device outputs the diagnostic information that is the candidate when the number of candidates for the diagnostic information is one.

[0148] According to this configuration, it is possible to output appropriate diagnostic information to an external device. (Supplementary Note 13) The diagnostic device according to any one of Supplementary Note 2 to Supplementary Note 12, wherein the processing device executes a new specific process based on the acquired parameters without using the acquired attribute information when the number of candidates for diagnostic information is zero.

[0149] According to this configuration, even if the number of candidates for diagnostic information is zero, a new specific process is executed without using the acquired attribute information, so that the number of candidates for diagnostic information can be set to one or more.

[0150] (Supplementary Note 14) The diagnostic device according to Supplementary Note 13, wherein the processing device allows the reception of new diagnostic information and outputs the new diagnostic information to the external device when the number of candidates for diagnostic information is zero even if a new specific process is executed.

[0151] According to this configuration, if the number of candidates for diagnostic information is zero even after a new identification process is executed, new diagnostic information input by an administrator or the like can be output to an external device.

[0152] (Supplementary Note 15) The diagnostic device according to any one of Supplementary Note 1 to Supplementary Note 14, wherein the diagnostic information includes an estimated cost of maintenance by maintenance type.

[0153] According to this configuration, the estimated cost of the maintenance can be output to an external device. (Supplementary Note 16) The diagnostic device according to any one of Supplementary Note 1 to Supplementary Note 15, wherein the diagnostic information includes a cost-effectiveness amount for performing the maintenance for each maintenance type.

[0154] According to this configuration, the cost-effectiveness amount can be output to an external device. (Supplementary Note 17) The diagnostic device according to any one of Supplementary Notes 1 to 16, wherein the processing device outputs maintenance information indicating that maintenance will be performed to a terminal of a worker who will perform maintenance on the wind power generation device.

[0155] According to this configuration, it is possible to have a worker or the like perform maintenance on the wind turbine generator 20 without requiring a user to perform maintenance ordering processing.

[0156] (Supplementary Note 18) A diagnostic method comprising: acquiring parameters used in diagnosing a wind power generation device as acquired parameters; acquiring attribute information of the wind power generation device as acquired attribute information; using correspondence information to identify diagnostic information based on the acquired parameters and the acquired attribute information; and outputting the diagnostic information to an external device, wherein the correspondence information is information in which a combination of the diagnostic parameters of the wind power generation device and the attribute information of the wind power generation device is associated with diagnostic information including at least one of a diagnosis result regarding an abnormality in the wind power generation device and a maintenance type of the wind power generation device.

[0157] 10 Management system, 20 Wind power generation device, 30 Collection device, 45 Wind power generation unit, 50 User terminal, 70 Maintenance terminal, 100 Diagnosis device, 102 Processing device, 104 Memory, 106 Communication interface, 108 Input device, 110 Display device, 112 Receiving unit, 114 Processing unit, 116 Transmitting unit, 118 Storage unit, 141 First database, 142 Second database, 143 Third database, 144 Calculation formula, 300 Report, 301 Diagnosis result information, 302 Maintenance type information, 303 Estimated amount information, 304 Cost-effectiveness information, 305 Maintenance confirmation information, 400 Input screen, 401 First information, 402 Second information.

Claims

1. An interface that acquires parameters used for diagnosing a wind power generation device as acquisition parameters and acquires attribute information of the wind power generation device as acquisition attribute information, a combination of the diagnosis parameters of the wind power generation device and the attribute information of the wind power generation device, and diagnosis information including at least one of a diagnosis result regarding an abnormality of the wind power generation device and a maintenance type of the wind power generation device. A diagnostic device comprising: a memory that stores correspondence information in which the correspondence information is associated; and a processing device that specifies the diagnostic information based on the acquisition parameters and the acquisition attribute information using the correspondence information and outputs the diagnostic information to an external device.

2. The correspondence information is a database in which a plurality of the combinations and a plurality of the diagnostic information corresponding to each of the plurality of combinations are associated. The processing device executes a specifying process for specifying candidates for the diagnostic information by referring to the database, and specifies the diagnostic information from the candidates for the diagnostic information. The diagnostic device according to claim 1.

3. The processing device executes an estimation process for estimating at least one of the life of a target component of the wind power generation device and the damage level of the target component, and executes the specifying process by referring to the database using the estimation result of the estimation process as a search condition. The diagnostic device according to claim 2.

4. In the database, an appropriateness degree indicating the appropriateness of each of the plurality of diagnostic information is associated with each of the plurality of diagnostic information. When the number of candidates for the diagnostic information is two or more, the processing device specifies the diagnostic information from the candidates for the diagnostic information based on the appropriateness degree associated with the diagnostic information that has become the candidate. The diagnostic device according to claim 2 or claim 3.

5. After outputting the diagnostic information to an external device, the processing device allows acceptance of appropriateness degree information regarding the appropriateness degree for the diagnostic information to a user terminal, and updates the appropriateness degree based on the appropriateness degree information. The diagnostic device according to claim 4.

6. When the number of candidates for the diagnosis information is two or more, the processing device specifies the diagnosis information having the maximum appropriateness from the two or more candidates. The diagnosis information includes the diagnosis result and the maintenance type. The appropriateness information includes first information indicating whether the maintenance of the maintenance type could be executed. The processing device updates the appropriateness to the minimum value when the first information indicates that the maintenance has not been executed. The diagnostic device according to claim 5.

7. The processing device outputs advice information regarding the operation of the wind power generation device to the external device when the appropriateness information satisfies a predetermined criterion. The diagnostic device according to claim 5.

8. When the number of candidates for the diagnosis information is one, the processing device outputs the diagnosis information that is the candidate. The diagnostic device according to claim 2 or claim 3.

9. When the number of candidates for the diagnosis information is zero, the processing device executes a new specific process based on the acquired parameters without using the acquired attribute information. The diagnostic device according to claim 2 or claim 3.

10. When the number of candidates for the diagnosis information is zero even after the new specific process is executed, the processing device permits reception of new diagnosis information and outputs the new diagnosis information to the external device. The diagnostic device according to claim 9.

11. The diagnosis information includes an estimate of maintenance according to the maintenance type. The diagnostic device according to any one of claims 1 to 3.

12. The diagnosis information includes the amount of cost-effectiveness due to the maintenance according to the maintenance type. The diagnostic device according to any one of claims 1 to 3.

13. The processing device outputs maintenance information indicating that the maintenance is to be executed to the terminal of an operator who performs the maintenance of the wind power generation device. The diagnostic device according to any one of claims 1 to 3.

14. Obtaining parameters used for diagnosing a wind power generation device as acquisition parameters, and obtaining attribute information of the wind power generation device as acquisition attribute information, and using correspondence information in which a combination of the diagnostic parameters of the wind power generation device and the attribute information of the wind power generation device is associated with diagnostic information including at least one of a diagnostic result regarding an abnormality of the wind power generation device and a maintenance type of the wind power generation device, specifying the diagnostic information based on the acquisition parameters and the acquisition attribute information, and outputting the diagnostic information to an external device. A diagnostic method comprising the steps of:

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