Diagnostic device and diagnostic method

Abstract

The diagnostic device (100) of the present application stores a first database (141) that stores correspondence information corresponding to a combination of a feature of waveform data of a wind power generation device (20) and attribute information of the wind power generation device (20) and diagnostic result information and maintenance kind diagnostic information on an anomaly of the wind power generation device (20). The diagnostic device (100) determines diagnostic information based on the acquired parameters and the acquired attribute information using the first database (141), and outputs the diagnostic information to the user terminal (50).

Description

Technical Field

[0001] This disclosure relates to diagnostic devices and diagnostic methods. Background Technology

[0002] For example, Japanese Patent Application Publication No. 2013-185507 (Patent Document 1) discloses a condition monitoring system for a wind power generation device. This condition monitoring system includes a vibration sensor that detects vibration values ​​at the location of the wind power generation device. Based on the vibration values ​​detected by the vibration sensor, the condition monitoring system diagnoses whether any abnormalities exist. Then, the condition monitoring system displays the diagnostic results on the display unit of a monitoring terminal.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2013-185507 Summary of the Invention

[0006] The technical problem that the invention aims to solve

[0007] Existing condition monitoring systems for wind power plants only perform anomaly diagnosis on each measurement data point, which may result in a lack of high-precision diagnostics. Furthermore, these systems typically display only basic diagnostic results on a screen. Therefore, additional maintenance procedures for wind power plants require specialized personnel, potentially leading to significant time consumption.

[0008] This disclosure was made to solve the above-mentioned problems, and its purpose is to provide at least one of the following suggestions in the diagnosis of wind power generation equipment: high-precision diagnosis and high-precision maintenance.

[0009] Technical solutions to solve technical problems

[0010] The diagnostic device disclosed herein includes an interface, a memory, and a processing unit. The interface acquires parameters for diagnosing a wind power generation device as acquisition parameters, and acquires attribute information of the wind power generation device as acquisition attribute information. The memory stores correspondence information corresponding to combinations of diagnostic parameters and attribute information of the wind power generation device, wherein the diagnostic information includes at least one of diagnostic results related to anomalies of the wind power generation device and maintenance types of the wind power generation device. The processing unit uses the correspondence information to determine diagnostic information based on the acquisition parameters and acquisition attribute information, and outputs the diagnostic information to an external device.

[0011] The diagnostic method disclosed herein includes: acquiring parameters for diagnosing a wind power generation device as acquisition parameters, and acquiring attribute information of the wind power generation device as acquisition attribute information. Furthermore, the diagnostic method includes using corresponding information to determine diagnostic information based on the acquisition parameters and the acquisition attribute information, and outputting the diagnostic information to an external device. The corresponding information corresponds a combination of the diagnostic parameters and attribute information of the wind power generation device to the diagnostic information. The diagnostic information includes at least one of the following: diagnostic results related to anomalies of the wind power generation device and a maintenance type of the wind power generation device.

[0012] Invention Effects

[0013] According to this disclosure, at least one of the following suggestions is available for the diagnosis of wind power generation equipment: high-precision diagnosis and high-precision maintenance. Attached Figure Description

[0014] Figure 1 This is a diagram illustrating an example of the structure of the management system disclosed herein.

[0015] Figure 2 This is a diagram representing an example of the first database.

[0016] Figure 3 This is a diagram representing an example of a second database.

[0017] Figure 4 This is a diagram representing an example of a third-party database.

[0018] Figure 5 This is a functional block diagram of the diagnostic device.

[0019] Figure 6 This is a diagram representing an example of a report.

[0020] Figure 7 This is an example diagram of an input screen that represents appropriateness information.

[0021] Figure 8 This is a flowchart illustrating the main processes of the diagnostic device.

[0022] Figure 9 This is a flowchart representing the report creation process.

[0023] Figure 10 This is a flowchart for maintenance and confirmation processing.

[0024] Figure 11 This is a flowchart of the update process. Detailed Implementation

[0025] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Furthermore, in the following drawings, the same or equivalent parts will be labeled with the same reference numerals, and their descriptions will not be repeated.

[0026] <Implementation Method 1>

[0027] Figure 1 This is a diagram illustrating a structural example of the management system 10 according to Embodiment 1. The management system 10 of this disclosure includes M (M is an integer greater than or equal to 1) wind power generation units 45, a diagnostic device 100, a user terminal 50, a maintenance terminal 70, and a network NW. The collection device 30, the diagnostic device 100, and the user terminal 50, described later, can communicate with each other via the network NW and the maintenance terminal 70.

[0028] 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 greater than or equal to 1).

[0029] Each of the M wind power generation devices 20 is assigned identification information (ID) and attribute information. The identification information is used to identify the wind power generation device 20. The attribute information is, for example, information representing the attributes of the wind power generation device 20; in this embodiment, it is model information representing the type of wind power generation device 20. Additionally, the attribute information may also include, for example, the manufacturing time.

[0030] The wind power generation device 20 is a device that receives wind power to generate electricity. The wind power generation device 20 includes a main bearing, a generator, and a speed increaser. Each of the vibration sensors Sn detects the vibration value of a specific part of the wind power generation device 20 (e.g., the main bearing, speed increaser, and generator). Furthermore, the vibration value is represented, for example, by one of the displacement, velocity, or acceleration of the specified part. The vibration value detected by the vibration sensor Sn, corresponding to the sensor ID of that vibration sensor, is output as time-series data to the collection device 30.

[0031] The time-series data collected by the collection device 30 is output to the diagnostic device 100. In addition, the wind power generation device ID and attribute information of the wind power generation device 20 corresponding to the collection device 30 are output from the collection device 30 to the diagnostic device 100 in correspondence with the time-series data.

[0032] User terminal 50 is a terminal device owned by user A. "User A" typically refers to the person who owns the wind power generation device 20, such as a power generation company. User terminal 50 is typically a mobile terminal that user A can carry. Alternatively, user terminal 50 can also be a dedicated computer terminal.

[0033] When the diagnostic device 100 detects an anomaly in the wind power generation unit 20, the operator will perform maintenance on the wind power generation unit 20. The maintenance terminal 70 is the terminal where the maintenance service provider's maintenance management personnel C operate. For example, the maintenance terminal 70 is the terminal that accepts the maintenance request. Specifically, when the diagnostic device 100 determines that maintenance of the wind power generation unit 20 is required, it sends maintenance information for requesting the maintenance to the maintenance terminal 70. The diagnostic device 100, referring to a prescribed database (not shown), the operator's work plan, and the delivery date of the parts to be repaired, formulates an engineering plan, including a schedule for the work and parts arrangement. Based on these processes, the diagnostic device 100 generates maintenance information.

[0034] The display unit of the maintenance terminal 70 displays a request image based on the maintenance information. This request image includes, for example, the address of the wind power generation unit 20 to be maintained, the date and time of maintenance, and the type of maintenance (work content) described later. The maintenance management personnel C, having confirmed the request image, arranges for operators to arrive at the address and date and time displayed on the image. "Operators" typically refers to personnel performing maintenance on the wind power generation unit 20 for any abnormalities. The operators perform the maintenance on the wind power generation unit 20 according to the work plan.

[0035] The diagnostic device 100 determines the diagnostic results of the anomaly diagnosis and the type of maintenance, etc., through calculations described later. Then, the diagnostic device 100 sends a report containing the diagnostic results of the anomaly diagnosis and the type of maintenance. Figure 6 The report 300 is sent to the user terminal 50. The user terminal 50 corresponds to the "external device" of this disclosure. In addition, the diagnostic device 100 is the terminal operated by the administrator B.

[0036] By displaying the report on the display unit, user terminal 50 enables user A to identify the diagnostic results and maintenance type of the anomaly. Additionally, diagnostic device 100 sends maintenance information, including the location of the anomaly, to maintenance terminal 70. This allows maintenance manager C to proactively identify anomalies.

[0037] The diagnostic device 100 includes a processing unit 102, a memory 104, and a communication interface 106. Furthermore, the communication interface... Figure 1 This is referred to as "Communication I / F". The processing unit 102 performs various processing and calculations. The various structural elements are interconnected via a data bus. The memory 104 includes ROM (Read Only Memory) and RAM (Random Access Memory), etc.

[0038] The processing device 102 is composed of a CPU (Central Processing Unit), an FPGA (Field-Programmable Gate Array), and a GPU (Graphics Processing Unit). Alternatively, the processing device 102 may be composed of at least one of a CPU, FPGA, and GPU. Furthermore, the processing device 102 may also be composed of processing circuitry. The processing device 102 is also referred to as "at least one processor" or "processing circuitry".

[0039] The memory 104 includes a volatile storage area (e.g., a working area) that temporarily stores program code, work records, etc., each time the processing device 102 executes any program. For example, the memory 104 includes RAM (Random Access Memory) and ROM (Read Only Memory).

[0040] ROM stores the program executed by the processing device 102. RAM temporarily stores data and other data generated by executing the program in the processing device 102. RAM can function as a temporary data memory, which is used as a working area.

[0041] The communication interface 106 is configured to communicate with external devices of the diagnostic device 100 (such as the collection device 30, the user terminal 50, and the maintenance terminal 70).

[0042] The diagnostic device 100 is connected to an input device 108 and a display device 110. The input device 108 may be, for example, a mouse or keyboard. The diagnostic administrator B can use the input device 108 to input specified information. The display device 110 displays the specified information.

[0043] [database]

[0044] Next, the database used in this embodiment will be described. A database is also called a table. Figure 2 This is a diagram representing an example of a first database (DB).

[0045] The first database is pre-created based on past diagnostic results from the diagnostic device 100 and simulations using the diagnostic device 100. Figure 2In the example, the following diagnostic information corresponds to each type of wind turbine. This diagnostic information includes waveform data characteristics, damage level, lifespan of the component being diagnosed, diagnostic results, recommended maintenance type, suggestions, and appropriateness. Furthermore, each piece of diagnostic information is assigned a diagnostic ID. That is, a diagnostic ID corresponds to one piece of diagnostic information. Although in Figure 2 Although not illustrated, the first database is configured as a diagnostic information database, and the diagnostic device 100 can also identify the diagnostic target component.

[0046] The type information A of the wind power generation device is as described above. The waveform data has a characteristic quantity B and an impaired frequency F. The characteristic quantity B is calculated through a prescribed operation. Specifically, the prescribed operation is used to calculate at least one of the following: 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 overall value of the waveform data.

[0047] Furthermore, the characteristic quantity B can be calculated using either the original waveform data or waveform data obtained by applying a specified bandpass filter to the original waveform data. Figure 2 In the example, as characteristic quantity B, characteristic quantities B1 to B6 are defined.

[0048] The damage frequency F is the frequency of vibration caused by damage, determined by the internal specifications of the components of the wind turbine 20 and the rotational frequency of the mounting shaft. The damage frequency F is the frequency at which the energy reaches its peak in the frequency spectrum, corresponding to the damaged component. The frequency spectrum is the vibration data detected by the vibration sensor Sn, for example, after performing a Fast Fourier Transform (FFT).

[0049] Damage level indicates the degree of damage to the component being diagnosed. Lifespan indicates the length of time the component being diagnosed will remain operational. Lifespan is the number of rotations and duration of time the blades of the component can operate continuously. Diagnostic results indicate the diagnostic findings related to the anomalies in the wind turbine 20. Maintenance type indicates the recommended maintenance type for the component being diagnosed. Recommendations indicate the recommendations to be given to the user during the continuous operation of the wind turbine 20. Appropriateness indicates the degree (level) of appropriateness of the diagnostic information (diagnostic results and maintenance types). The technical significance of appropriateness will be described later.

[0050] Figure 2 The “machine type information of wind power generation device” corresponds to the “attribute information of wind power generation device” in this disclosure. Figure 2 The “characteristics of waveform data” correspond to the “diagnostic parameters of wind power generation devices” in this disclosure. Figure 2The “diagnostic results,” “maintenance type,” and “recommendations” correspond to the “diagnostic information” in this disclosure. Figure 2 The first database corresponds to the "corresponding information" of this disclosure. Thus, Figure 2 The first database corresponds to information that links "the combination of attribute information and diagnostic parameters of the wind power generation device" with "diagnostic results and maintenance types of the wind power generation device 20". From different perspectives, the first database corresponds search condition groups with diagnostic information groups. The search condition group includes "the combination of attribute information of the wind power generation device, diagnostic parameters of the wind power generation device, damage level, and lifespan". The diagnostic information group contains "diagnostic results and maintenance types of the wind power generation device 20". The diagnostic information group is also the search result based on the search conditions.

[0051] Figure 3 This is a diagram representing an example of a second database. In this second database, wind power generator IDs correspond to user terminal IDs. For example, wind power generator ID: W1 corresponds to user terminal ID: U1. Figure 4 This is a diagram illustrating an example of a third-party database. In this database, maintenance IDs correspond to estimated amounts. For example, maintenance ID: M1 corresponds to estimated amount: N1.

[0052] [Functional block diagram of the diagnostic device]

[0053] Figure 5 This is a functional block diagram of the diagnostic device 100. The diagnostic device 100 includes a receiving unit 112, a processing unit 114, a transmitting unit 116, and a storage unit 118. The receiving unit 112 and the transmitting unit 116 are connected to... Figure 1 Corresponding to the communication interface 106. The processing unit 114 and... Figure 1 Corresponding to the processing device 102. Storage unit 118 and... Figure 1 Corresponding to memory 104, at least a portion of the storage area of ​​memory 104 can be used.

[0054] Storage unit 118 stores Figure 2 The first database 141 shown Figure 3 The second database 142 shown Figure 4 The third database 143 and calculation formula 144 (formula (1) described later) are shown.

[0055] The receiving unit 112 acquires vibration data as time-series data, wind turbine type information, and wind turbine ID of the wind turbine 20 from the M collecting devices 30. This vibration data corresponds to the "acquisition parameters" of this disclosure. This vibration data is a parameter used for diagnosing the wind turbine 20. Furthermore, the type information corresponds to the "acquisition attribute information" of this disclosure.

[0056] The processing unit 114 detects the characteristics of the waveform data (characteristic quantities and the presence or absence of damage frequency peaks) by performing the aforementioned prescribed calculations on the vibration data (acquisition parameters). Furthermore, based on these characteristics, the processing unit 114 infers the damage level and lifespan of the component to be diagnosed through prescribed inference processing.

[0057] Then, the processing unit 114 uses machine model information, waveform data characteristics, damage level, and lifespan as keywords, and refers to the search criteria (first database 141) to determine diagnostic information. That is, the processing unit 114 determines diagnostic information based on vibration data (obtained parameters) and machine model information (obtained attribute information), referring to the search criteria (first database 141). Then, the processing unit 114 creates a report containing the determined diagnostic information. Additionally, the processing unit 114 also refers to the third database 143 to create the report.

[0058] Furthermore, the processing unit 114 uses the wind power generation device ID and refers to the second database 142 to determine the user terminal ID of the user terminal 50 to which the data is destined. Then, the processing unit 114 sends a report to the user terminal 50 represented by the determined user terminal ID via the sending unit 116.

[0059] Furthermore, if the abnormal diagnosis of the wind power generation device 20 indicates that maintenance is required, the processing unit 114 will send the aforementioned maintenance information to the maintenance terminal 70.

[0060] [Report]

[0061] Figure 6 This is a diagram illustrating an example of a report 300 created by the processing unit 114. The report 300 contains the diagnostic results (diagnostic information) from the processing unit 114. The report 300 is displayed as a report image on the user terminal 50. Specifically, the report 300 includes diagnostic result information 301, maintenance type information 302, estimated cost information 303, cost-effectiveness information 304, and maintenance confirmation information 305.

[0062] Diagnostic result information 301 indicates the results of a diagnosis using the first database 141. Figure 6 In the example, diagnostic result information 301 is the information that the diagnostic result is "moderate damage to the bearing assembly".

[0063] Maintenance type information 302 indicates the type of maintenance required. Figure 6 In the example, maintenance type information 302 indicates that the maintenance type is "Check C3".

[0064] Estimated Amount Information 303 represents the estimated amount for maintenance of the type of maintenance indicated by Maintenance Type Information 302. Figure 6 In the example, the estimated amount information 303 indicates that the estimated amount is "X1 yen".

[0065] Cost-benefit information 304 represents the cost-benefit of performing maintenance of the type indicated by maintenance type information 302. For example, diagnostic device 100 uses the lifespan evaluation results of wind power generation unit 20 to calculate the maintenance cost reduction effect (downtime reduction effect through prior scheduling) when immediately ordering maintenance for wind power generation unit 20. This calculation may use, for example, a prescribed database and formulas. Figure 6 In the example, cost-benefit information 304 indicates that the cost-benefit amount is "X2 yen".

[0066] Maintenance confirmation message 305 is used to receive information from the user regarding whether maintenance should be performed. Figure 6 In the example, maintenance confirmation message 305 includes the text "Do you want to perform maintenance?", a "Yes" button, and a "No" button.

[0067] When the user confirms the contents of report 300, they should press the "Yes" button if they wish to perform the maintenance indicated in maintenance type information 302, and press the "No" button if they do not wish to perform the maintenance.

[0068] When the user presses the "Yes" button, the user terminal 50 sends a "Yes" signal to the diagnostic device 100 indicating that the "Yes" button has been pressed. Upon receiving the "Yes" signal, the diagnostic device 100 sends maintenance information based on the content of the report 300 to the maintenance terminal 70.

[0069] On the other hand, when the user operates the "No" button, the user terminal 50 sends a "No" signal to the diagnostic device 100 indicating that the "No" button has been operated. After receiving the "No" signal, the diagnostic device 100 does not send maintenance information to the maintenance terminal 70.

[0070] [Appropriateness Information]

[0071] Next, the appropriateness information will be explained. Users can input information into user terminal 50... Figure 6 The report, along with the maintenance type information 302 in that report, provides appropriateness information related to the suitability (usefulness) of the performed maintenance. This input appropriateness information is sent to the diagnostic device 100. Based on the appropriateness information input by the user, the diagnostic device 100 updates the appropriateness corresponding to the determined diagnostic information (see reference). Figure 2 ).

[0072] First, let's explain the input screen for appropriateness information. Figure 7This is a diagram of an example of an input screen 400 representing appropriateness information. The diagnostic device 100, for example, will, after a certain period (e.g., one month) has elapsed since the report was sent, [the following is a separate, unrelated sentence:] Figure 7 The input screen 400 is displayed on the user terminal 50. In other words, the input screen 400 is for... Figure 6 The report and the questionnaire input screen for the maintenance that has been performed.

[0073] Reference Figure 7 The input screen 400 contains first information 401 and second information 402. The second information 402 also contains information 411, information 412, information 413 and information 414.

[0074] Information 401, 411, 412, 413, and 414 are information related to the user's question. Therefore, information 401, 411, 412, 413, and 414 are also referred to as the first question, the second question, the third question, the fourth question, and the fifth question, respectively (see reference). Figure 11 (Steps S304 and S306). A "Yes" button and a "No" button are displayed on each of the first information 401, information 411, information 412, information 413, and information 414. Then, the user selects either the "Yes" or "No" button on all of the first information 401, information 411, information 412, information 413, and information 414. Additionally, the user inquires with maintenance personnel about any unclear points regarding the first information 401, information 411, information 412, information 413, and information 414.

[0075] The first message 401 indicates whether execution is possible. Figure 6 The report recommends the types of maintenance required. Figure 7 In the image, the first information 401 is the text image "Is maintenance possible?". For example, when maintenance has been performed on the wind power generation device 20 (maintenance is possible), the user presses the "Yes" button. On the other hand, if maintenance has not been performed on the wind power generation device 20 due to structural problems or other reasons (maintenance cannot be performed), the user presses the "No" button.

[0076] The second piece of information 402 is information indicating user A's satisfaction with the report (diagnostic information). As mentioned above, it includes information 411, information 412, information 413, and information 414.

[0077] Information 411 indicates whether an anomaly in the wind power generation unit 20 has been detected by the operator. Figure 7 In the image, information 411 is the text "Has an anomaly been found in wind power generation device 20?". For example, in... Figure 6When an abnormality is found in the location indicated by the diagnostic results information 301 in the report, the user presses the "Yes" button. On the other hand, when no abnormality is found in the wind power generation device 20, the user presses the "No" button.

[0078] Information 412 indicates whether there have been any improvements through maintenance of the wind power generation unit 20. Figure 7 In the image, information 412 is the text image asking, "Was the wind power generation device improved through maintenance?" The improvement to the wind power generation device 20 achieved through maintenance could be, for example, an increase in the power generation of the wind power generation device 20 compared to before maintenance. If the wind power generation device 20 has been improved, the user presses the "Yes" button. Conversely, if the wind power generation device 20 has not been improved, the user presses the "No" button.

[0079] Information 413 relates to the appropriateness of the degree of anomaly in wind power generation equipment. Figure 7 In the image, information 413 is the text "Is the damage level appropriate?". For example, a user receives a damage level assessment from an operator who performed maintenance; if... Figure 6 If the damage level indicated in the report (diagnostic result information 301) is appropriate, the user clicks the "Yes" button. Conversely, if the damage level is inappropriate, the user clicks the "No" button.

[0080] Information 414 relates to the appropriateness of replacing components of the wind power generation unit 20. When the diagnostic device 100 performs component replacement and maintenance... Figure 2 When the replacement of components (such as bearings and speed increasers) is identified as a maintenance type, the diagnostic device 100 displays information 414 on the user terminal 50. If the component replacement or maintenance is appropriate, the user presses the "Yes" button. On the other hand, if the component replacement or maintenance is inappropriate, the user presses the "No" button.

[0081] The questions in Info 411 to Info 414 clearly indicate that clicking the "Yes" button signifies satisfaction with the report or maintenance content. Conversely, clicking the "No" button signifies dissatisfaction with the report or maintenance content.

[0082] In this way, users can input appropriateness information indicating how well they perceive the diagnostic result. Furthermore, for a single diagnostic record (diagnostic result and maintenance type), appropriateness information may sometimes be input by multiple users. Hereinafter, the number of appropriateness records input for a single diagnostic record will also be referred to as the "record count."

[0083] Next, the updating of the suitability information will be explained. User terminal 50 sends the suitability information input to it to diagnostic device 100. Processing unit 114 of diagnostic device 100 uses the suitability information and specified update information to calculate the suitability. Then, processing unit 114 updates the stored suitability E to the calculated suitability.

[0084] Update information is, for example, Figure 5 The specified calculation formula 144 (function) is shown. More specifically, this calculation formula 144 is a formula that updates the suitability to an extremely low value when the first information 401 indicates that maintenance has not been performed. In this disclosure, "extremely low value" is set to the minimum value of the suitability (e.g., "0"). This calculation formula 144 is a formula that updates the suitability to an increased value when the second information 402 indicates that the satisfaction level is high. For example, calculation formula 144 is represented by the following formula (1).

[0085] Appropriateness E = a × ((b + c + d + e) ​​ / N) (1)

[0086] Here, as mentioned above, the minimum value of appropriateness E is “0”. Furthermore, “a” in equation (1) is a value that is “1” when the “Yes” button is operated in the first information 401 and a value that is “0” when the “No” button is operated in the first information 401.

[0087] Furthermore, N in Equation (1) is the total number of records for a single diagnostic message corresponding to the calculated appropriateness E. "b", "c", "d", and "e" in Equation (1) represent the number of records in messages 411, 412, 413, and 414 that were marked "yes," respectively.

[0088] [flow chart]

[0089] Figure 8 This is a flowchart illustrating the main processes of the diagnostic device 100 in this embodiment. Additionally, Figure 8 The three dots ellipsis indicate that a certain period of time has elapsed. Furthermore, Figure 8 The processing is performed at specified intervals. These specified intervals may be, for example, the period for accumulating time-series data required to diagnose anomalies in the wind power generation unit 20.

[0090] In step S2, the diagnostic device 100 performs a report creation process. The report creation process is primarily used to create... Figure 6 The report is processed. Next, in step S4, the diagnostic device 100 performs a maintenance confirmation process. The maintenance confirmation process is mainly used to confirm whether maintenance should be performed. Next, in step S6, the diagnostic device 100 performs an update process. The update process mainly updates... Figure 2 The first database processing.

[0091] Figure 9 This is a flowchart illustrating the report creation process in step S2. First, in step S102, the diagnostic device 100 acquires the attribute information of the wind power generation device 20 (acquiring attribute information), vibration data for a specified period (accumulation period) (acquiring parameters), and the wind power generation device ID from the collection device 30. The vibration data for a certain period is the time series data described above.

[0092] Next, in step S104, the diagnostic device 100 calculates the characteristic quantity based on the time series data by performing the aforementioned prescribed calculation.

[0093] Next, in step S106, the diagnostic device 100 determines whether the characteristic quantity exceeds a threshold. If the determination in step S106 is "no," the wind power generation device 20 under diagnosis has no abnormalities, therefore... Figure 9 The processing ends. On the other hand, if the determination in step S106 is "yes", the processing proceeds to step S108.

[0094] In step S108, the diagnostic device 100 performs peak determination processing. Specifically, the diagnostic device 100 generates a spectrum by performing a Fast Fourier Transform on the time series data. Then, the diagnostic device 100 uses a bandpass filter to calculate the peak energy of the damage frequency of each component. Then, when the peak energy exceeds a second threshold, the diagnostic device 100 determines that there is a peak value consistent with the damage frequency (i.e., a damaged component exists). In addition, the damage frequency may contain higher harmonic components.

[0095] In step S110, the diagnostic device 100 performs a prediction process. As described above, the prediction process is a process that predicts the damage level and lifespan of the component being diagnosed based on the characteristics of waveform data (characteristic quantities and the presence or absence of damage frequency peaks). The prediction process is performed, for example, through a prescribed calculation.

[0096] Next, in step S112, the diagnostic device 100 temporarily stores the search criteria in, for example, the RAM described above. The search criteria include the model information of the wind power generation device 20, the characteristics of the waveform data (the number of features exceeding the first threshold and the presence or absence of peaks determined in step S108), and the estimated results of step S110 (damage level and lifespan).

[0097] Next, in step S114, the first database (referencing) is searched using the search criteria stored in step S112. Figure 2 This involves performing a determination process to identify diagnostic information. This determination process is also the "retrieval process" for retrieving diagnostic information.

[0098] Here, regarding the determination and processing of diagnostic information (the retrieval and processing of diagnostic information), judgment is made. Figure 2 Does the search condition group contain any search conditions that match the search conditions in step S112? Furthermore, Figure 2 If a search condition in the search condition group is completely identical to the search condition in step S112, the number of candidate diagnostic information (hit count) is the number of completely identical search conditions. Furthermore, in Figure 2 If a similar search condition exists in the search condition group to the search condition in step S112, the number of candidate diagnostic information is the number of such similar search conditions. Additionally, in Figure 2 If there are no search conditions in the search condition group that are the same as or similar to the search conditions in step S112, the number of candidates for diagnostic information is "0".

[0099] In step S116, the diagnostic device 100 determines whether the number of candidate diagnostic information is two or more. If the number of candidates is two or more (yes in step S116), in step S118, the diagnostic device 100 determines diagnostic information from the candidates based on the appropriateness corresponding to the candidate diagnostic information. Specifically, in step S118, the diagnostic device 100 determines an appropriateness E (refer to...) from two or more candidate diagnostic information. Figure 2 The most important diagnostic information is obtained. Then, the process proceeds to step S130.

[0100] Furthermore, in step S116, if the number of candidate diagnostic information is not more than 2 ("No" in step S116), in step S120, the diagnostic device 100 determines whether the number of candidate diagnostic information is 1. If the number of candidate diagnostic information is 1 ("Yes" in step S120), in step S122, the diagnostic device 100 determines the diagnostic information as the 1 candidate.

[0101] Furthermore, in step S120, if the number of candidate diagnostic information is not 1 (in step S120 it is "No"), the process proceeds to step S124. The case where the number of candidate diagnostic information is not 1 refers to the case where the number of candidate diagnostic information is "0".

[0102] In step S124, the diagnostic device 100 determines whether the search criteria contain model information. In the first determination of step S124, since the search criteria contain model information, the determination is "yes". Next, in step S128, the model information is excluded (deleted) from the search criteria, and a new search for diagnostic information is performed.

[0103] Then, the diagnostic device 100 executes steps S116 and S120, etc. Furthermore, in step S124 (the second determination in step S124) after both step S116 and S120 are determined to be "no", since the search criteria do not include model information, the determination in step S124 is "no". In this case, no matter how many times the search process is executed, the number of candidate diagnostic information will not be more than 1. Therefore, in step S126, the diagnostic device 100 allows the manager B (technical personnel) to input diagnostic information. In step S126, the diagnostic device 100 displays the diagnostic information on the display device 110 (see reference). Figure 1 The display shows the text "Please enter diagnostic information". Administrator B uses input device 108 to input diagnostic information into the display device 110.

[0104] In step S130, based on any one of the diagnostic information determined in step S118, the diagnostic information determined in step S122, and the diagnostic information input in step S126, the estimated maintenance cost and cost-effectiveness are calculated. Next, in step S132, the diagnostic device 100 generates... Figure 6 The report 300 is generated and sent to the user terminal 50. The report 300 includes: any one of the diagnostic information determined in step S118, the diagnostic information determined in step S122, and the diagnostic information input in step S126; and the estimated maintenance cost and cost-effectiveness calculated in step S130. The report creation process then ends.

[0105] Figure 10 This is a flowchart of the maintenance confirmation process in step S4. The maintenance confirmation process is... Figure 9 The process is executed when the "Yes" or "No" button in the maintenance confirmation information 305 of the output report 300 in step S132 is operated.

[0106] In step S204, the diagnostic device 100 determines whether maintenance is required. In step S204, the device is determined to be "yes" when the "yes" button in the maintenance confirmation information 305 is pressed, and "no" when the "no" button is pressed.

[0107] If the determination is "yes" in step S204, in step S206 the diagnostic device 100 will send maintenance information (refer to...) Figure 5 The message is sent to maintenance terminal 70. If the result is "no" in step S204 and the processing in step S206 is completed, the maintenance confirmation process ends.

[0108] Figure 11 This is a flowchart of the update process in step S6. The update process is initiated by user A. Figure 7The appropriateness information is sent to the diagnostic device 100 for execution.

[0109] In step S302, the diagnostic device 100 acquires the appropriateness information input by the user. Next, in step S304, the diagnostic device 100 determines whether the answer to the first question (first information 401) is "yes".

[0110] Here, the case where "No" is determined in step S304 is when a maintenance type that cannot be performed is recommended to the user. In this case, the user will feel uncomfortable. Therefore, in step S308, the diagnostic information determination process (retrieval process) is performed again.

[0111] In step S308, diagnostic information corresponding to the second-highest appropriateness of the diagnostic information most recently determined in step S118 or step S122 is determined. Then, the process returns. Figure 9 Step S130. In step S130, the diagnostic device 100 performs the processing of step S132 based on the diagnostic information determined in step S308.

[0112] If the determination is "yes" in step S304, in step S306, the diagnostic device 100 determines the second to fifth problems ( Figure 7 Whether all information 411 to 414 are "No". A condition determined as "Yes" in step S306 is when the user's satisfaction with the diagnostic information is extremely low. In this case, the user will feel uncomfortable. Therefore, in this case, the diagnostic device 100 executes the processing in step S308.

[0113] If the determination in step S306 is "no", the process proceeds to step S310. The case where the determination in step S306 is "no" corresponds to the case where "the suitability information meets the prescribed benchmark" in this disclosure.

[0114] In step S310, the diagnostic device 100 includes suggestions (refer to) in the determined diagnostic information. Figure 2 The information is sent to the user terminal 50. Then, in step S312, the combination of the search criteria stored in step S112 and the determined diagnostic information is appended and stored in the first database. Furthermore, the appropriateness corresponding to this combination is set to a predetermined value. Further, in step S312, the diagnostic device 100 uses the above formula (1) to update the appropriateness corresponding to the determined diagnostic information.

[0115] [Summarize]

[0116] (1) The diagnostic device 100 of this embodiment acquires parameters used for diagnosis in the wind power generation device as acquisition parameters. Simultaneously, the diagnostic device 100 acquires attribute information of the wind power generation device 20 as acquisition attribute information. The diagnostic device 100 has a memory 104 storing a first database 141. The first database 141 corresponds the combination of diagnostic parameters and attribute information of the wind power generation device 20 with diagnostic information, which includes diagnostic results related to anomalies in the wind power generation device 20 and the maintenance type of the wind power generation device 20. The processing device (processing unit 114) uses the first database 141 to determine diagnostic information based on the acquisition parameters and acquisition attribute information, and outputs the diagnostic information to the user terminal 50.

[0117] For example, if a diagnostic device, like a conventional diagnostic device, performs a diagnosis using only the diagnostic parameters of the wind power generation device 20 without using its attribute information, the accuracy of the diagnostic results may be reduced due to an excessive number of candidate results. In contrast, the diagnostic device 100 of this embodiment uses past diagnostic results (first database) to determine the diagnostic results based not only on the diagnostic parameters of the wind power generation device 20 but also on its attribute information. Therefore, the diagnostic device 100 can automatically perform high-precision diagnosis for damage that frequently occurs in wind power generation devices with similar attributes. Therefore, compared to diagnosis using only the diagnostic parameters of the wind power generation device 20 without using its attribute information, the diagnostic device 100 of this embodiment can perform a higher-precision diagnosis. Furthermore, the diagnostic device 100 of this embodiment can also determine recommended maintenance from past maintenance examples. Depending on the wind power generation device, there are maintenance procedures that cannot be performed structurally. By utilizing past data accumulated in the database, it is possible to automatically determine maintenance suitable for the same type of wind power generation device. Therefore, since there is no need for technicians or other personnel to design and maintain wind power generation equipment, rapid maintenance services can be provided, thus eliminating the problem of time-consuming maintenance.

[0118] (2) The diagnostic device 100 has a pre-created first database 141 and performs a determination process for determining candidates for diagnostic information with reference to the first database 141. Then, the diagnostic device 100 determines the diagnostic information from the candidates for diagnostic information.

[0119] Based on this structure, it is possible to use a pre-created first database 141 to determine candidates for diagnostic information, thereby determining the diagnostic information.

[0120] (3) The diagnostic device 100 performs speculative processing. Figure 9In step S110), the estimation process estimates at least one of the lifespan of the target component of the wind power generation device 20 and the damage level of the target component. Then, the diagnostic device 100 uses the estimation result of the estimation process as a search condition and performs a determination process with reference to the first database 141. Figure 9 Step S114).

[0121] Based on this structure, the accuracy of diagnostic information can be improved by combining the inference results of the current inference processing with the database created in the past to determine the diagnostic information.

[0122] (4) Figure 2 As shown, in the first database 141, each of the multiple diagnostic information corresponds to an appropriateness degree E representing the appropriateness of the diagnostic information. When the number of candidates for diagnostic information is 2 or more ("Yes" in step S116), the diagnostic device 100 determines diagnostic information from the candidates based on the appropriateness degree corresponding to the diagnostic information that is a candidate (step S118).

[0123] Based on this structure, when the number of candidate diagnostic information is two or more, diagnostic information can be screened based on the appropriateness corresponding to the candidate diagnostic information, thereby improving the accuracy of diagnostic information.

[0124] (5) The diagnostic device 100 transmits diagnostic information ( Figure 6 After the report (300) is output to an external device (user terminal 50), the user terminal is allowed to receive appropriateness information related to the appropriateness of the diagnostic information (see reference). Figure 7 The diagnostic device 100 updates the suitability score based on the suitability information. Figure 11 Step S312).

[0125] This structure allows the user's intent to be reflected in the appropriateness level, thus improving both user convenience and the accuracy of diagnostic information.

[0126] (6) When the number of candidate diagnostic information is two or more, the diagnostic device 100 determines the diagnostic information with the highest appropriateness from the two or more candidates (step S118). Furthermore, as... Figure 7 As shown, the suitability information includes first information 401 indicating whether maintenance of a certain type can be performed. If the first information 401 indicates that maintenance has not been performed, the diagnostic device 100 updates the suitability to the minimum value (refer to the calculation formula 144 of the above formula (1)).

[0127] Based on this structure, maintenance types that cannot be performed can be output to external devices.

[0128] (7) Figure 7 As shown, the suitability information includes second information 402 indicating the user's satisfaction with the report 300 of the wind power generation device 20. If the second information indicates high satisfaction, the diagnostic device 100 updates the suitability to increase (refer to the calculation formula 144 as the above formula (1)).

[0129] Based on this structure, diagnostic information that enables high user satisfaction can be easily output to external devices.

[0130] (8) Figure 7 As shown, the second information 402 includes information 411, information 412, information 413, and information 414. Information 411 indicates whether an anomaly has been detected in the wind power generation device 20. Information 412 indicates whether improvements have been made through maintenance of the wind power generation device 20. Information 413 is related to the appropriateness of the degree of anomaly in the wind power generation device. Information 414 is related to the appropriateness of replacing components of the wind power generation device 20.

[0131] Based on this structure, information 411, information 412, information 413 and information 414 can be reflected in the appropriateness.

[0132] (9) When the suitability information meets the prescribed benchmark (No in step S306), the diagnostic device 100 outputs the suggested information related to the operation of the wind power generation device 20 to the user terminal 50 (step S310).

[0133] Based on this structure, users can identify recommended information related to the operation of the wind power generation device 20.

[0134] (10) When the suitability information does not meet the specified criteria ("No" in step S304 or "Yes" in step S306), the diagnostic device 100 excludes the determined diagnostic information from multiple diagnostic information and performs the determination process again based on suitability (step S308).

[0135] Based on this structure, when the suitability information does not meet the prescribed criteria, the process of determining diagnostic information is performed again. That is, the diagnostic device 100 can perform a search for similar cases in other models and can output diagnostic information that meets the prescribed criteria to an external device.

[0136] (11) The diagnostic device 100 repeats the determination process until the suitability information meets the specified criteria (until it is determined to be "no" in step S306, the processes of steps S130, S132, S204, S206, S302, S304, S306, and S308 are repeated).

[0137] Based on this structure, diagnostic information that meets the prescribed criteria can be output to an external device.

[0138] (12) When the number of candidates for diagnostic information is 1 ("Yes" in step S120), the diagnostic device 100 outputs the diagnostic information as the candidate (step S122).

[0139] Based on this structure, appropriate diagnostic information can be output to external devices.

[0140] (13) When the number of candidates for diagnostic information is 0 (No in step S120), the diagnostic device 100 does not use the model information (acquire attribute information) but performs a new determination process based on the acquired parameters (step S128).

[0141] Based on this structure, even if the number of candidates for diagnostic information is 0, the number of candidates for diagnostic information can be increased to 1 or more because new determination processing is performed without using the acquisition attribute information.

[0142] (14) Even if a new determination process is performed and the number of candidate diagnostic information is still 0 (if the process of step S128 is performed but the result is "no" in step S120), the diagnostic device 100 allows the input of new diagnostic information (step S126). Then, the diagnostic device 100 outputs the new diagnostic information to the user terminal 50.

[0143] Based on this structure, even if a new determination process is performed and the number of candidate diagnostic information is still 0, new diagnostic information input by the administrator or others can be output to an external device.

[0144] (15) Diagnostic information (report 300) includes the estimated cost of maintenance based on the type of maintenance (estimated cost information 303).

[0145] Based on this structure, the estimated maintenance cost can be output to an external device.

[0146] (16) Diagnostic information (report 300) includes the amount of cost benefits generated by performing maintenance based on maintenance type (cost benefit information 304).

[0147] With this structure, cost-effective amounts can be output to external devices.

[0148] The diagnostic device 100 outputs maintenance information indicating the performance of the maintenance to the maintenance terminal 70 of the operator performing the maintenance of the wind power generation device. Figure 10 Step S206).

[0149] With this structure, operators can perform maintenance on the wind power generation unit 20 without the need for users to process maintenance orders.

[0150] [Variation Example]

[0151] (1) In the above embodiment, the structure of a vibration sensor used to detect whether there is any abnormality in the wind power generation device 20 has been described. However, this sensor may also be other sensors. Other sensors include, for example, temperature sensors, AE (Acoustic Emission) sensors, displacement sensors, or sound sensors.

[0152] (2) In the above embodiment, the corresponding information was described as the first database 141. However, the corresponding information may also be other information. For example, the corresponding information may also be a function that outputs diagnostic results and maintenance types if the model information and waveform data characteristics of the wind power generation device 20 are input.

[0153] [Postscript]

[0154] All embodiments disclosed herein should be considered illustrative rather than limiting. The scope of the invention should be understood not by the description of the above embodiments, but by the claims, including all variations within the same meaning and scope as the claims.

[0155] (Appendix 1) A diagnostic device includes: an interface that acquires parameters 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 memory that stores corresponding information for a combination of diagnostic parameters and attribute information of the wind power generation device and diagnostic information, the diagnostic information including at least one of diagnostic results related to anomalies of the wind power generation device and maintenance type of the wind power generation device; and a processing device that uses the corresponding information to determine diagnostic information based on the acquisition parameters and acquisition attribute information and outputs the diagnostic information to an external device.

[0156] Based on this structure, not only diagnostic parameters of the wind power generation device but also attribute information of the wind power generation device are used to determine diagnostic information that includes diagnostic results related to anomalies of the wind power generation device and at least one of the maintenance types of the wind power generation device. Therefore, the accuracy of both the diagnostic results and at least one of the maintenance types can be improved.

[0157] (Note 2) The diagnostic apparatus as described in Note 1, wherein the corresponding information is a database that corresponds multiple combinations to multiple diagnostic information corresponding to the multiple combinations, and the processing apparatus performs a determination process for determining candidates of diagnostic information by referring to the database, and determines diagnostic information from the candidates of diagnostic information.

[0158] Based on this structure, a pre-created database can be used to identify candidates for diagnostic information, thereby determining the diagnostic information.

[0159] (Note 3) The diagnostic device as described in Note 2, wherein the processing device performs a speculative process that speculates on at least one of the lifespan of the object component of the wind power generation device and the damage level of the object component, and performs a determination process by referring to a database using the speculative result of the speculative process as a search condition.

[0160] Based on this structure, the accuracy of diagnostic information can be improved by combining the prediction results of the current prediction process with the database created in the past to determine the diagnostic information.

[0161] (Note 4) The diagnostic apparatus as described in Note 2 or 3, wherein each of the plurality of diagnostic information in the database corresponds to a degree of appropriateness representing the appropriateness of the diagnostic information, and the processing apparatus determines the diagnostic information from the candidates of diagnostic information based on the degree of appropriateness corresponding to the candidate diagnostic information when the number of candidates of diagnostic information is 2 or more.

[0162] Based on this structure, when the number of candidate diagnostic information is two or more, diagnostic information can be screened based on the appropriateness corresponding to the candidate diagnostic information, thereby improving the accuracy of diagnostic information.

[0163] (Note 5) The diagnostic device as described in Note 4, wherein after the processing device outputs diagnostic information to an external device, it allows a user terminal to receive appropriateness information related to the appropriateness of the diagnostic information, and updates the appropriateness based on the appropriateness information.

[0164] This structure allows the user's intent to be reflected in the appropriateness level, thus improving both user convenience and the accuracy of diagnostic information.

[0165] (Note 6) The diagnostic device as described in Note 5, wherein when the number of candidates for diagnostic information is 2 or more, the processing device determines the diagnostic information with the highest appropriateness from the 2 or more candidates, the diagnostic information includes a diagnostic result and a maintenance type, the appropriateness information includes first information indicating whether the maintenance of the maintenance type can be performed, and the processing device updates the appropriateness to the minimum value when the first information is information indicating that the maintenance has not been performed.

[0166] Based on this structure, maintenance types that cannot be performed can be output to external devices.

[0167] (Note 7) The diagnostic device as described in Note 6, wherein the suitability information includes second information indicating the user's satisfaction with the diagnostic information of the wind power generation device, and the processing device updates the suitability to increase if the second information indicates high satisfaction.

[0168] Based on this structure, diagnostic information that enables high user satisfaction can be easily output to external devices.

[0169] (Note 8) The diagnostic device as described in Note 7, wherein the second information includes at least one of: information indicating whether an abnormality of the wind power generation device has been detected; information indicating whether there has been improvement due to maintenance of the wind power generation device; information related to the appropriateness of the degree of abnormality of the wind power generation device; and information related to the appropriateness of component replacement of the wind power generation device.

[0170] Based on this structure, at least one of these four pieces of information can be reflected in the appropriateness.

[0171] (Note 9) The diagnostic device as described in any one of Notes 5 to 8, wherein the processing device outputs suggested information related to the operation of the wind power generation device to an external device when the suitability information meets the prescribed criteria.

[0172] This structure enables users to identify recommended information related to the operation of wind power generation devices.

[0173] (Note 10) The diagnostic device as described in Note 9, wherein when the suitability information does not meet the prescribed criteria, the processing device performs determination processing again based on suitability from the diagnostic information after excluding the determined diagnostic information from multiple diagnostic information.

[0174] Based on this structure, if the suitability information does not meet the specified criteria, the process of determining the diagnostic information is performed again, so that the diagnostic information that meets the specified criteria can be output to an external device.

[0175] (Note 11) The diagnostic device as described in Note 10, wherein the processing device repeatedly performs the determination process until the suitability information meets the specified criteria.

[0176] Based on this structure, diagnostic information that meets the prescribed criteria can be output to an external device.

[0177] (Note 12) The diagnostic apparatus as described in any one of Notes 2 to 11, wherein the processing device outputs diagnostic information as a candidate when the number of candidates for diagnostic information is 1.

[0178] Based on this structure, appropriate diagnostic information can be output to external devices.

[0179] (Note 13) The diagnostic apparatus as described in any one of Notes 2 to 12, wherein the processing apparatus performs a new determination process based on the acquisition parameters without using the acquisition attribute information when the number of candidates for diagnostic information is 0.

[0180] Based on this structure, even if the number of candidates for diagnostic information is 0, the number of candidates for diagnostic information can be increased to 1 or more because new determination processing is performed without using the acquisition attribute information.

[0181] (Note 14) The diagnostic apparatus as described in Note 13, wherein the processing device allows receiving new diagnostic information and outputs the new diagnostic information to an external device even when a new determination process has been performed and the number of candidates for diagnostic information is still 0.

[0182] Based on this structure, even if a new determination process is performed and the number of candidate diagnostic information is still 0, new diagnostic information input by the administrator or others can be output to an external device.

[0183] (Note 15) The diagnostic device as described in any one of Notes 1 to 14, wherein the diagnostic information includes the estimated cost of maintenance based on the type of maintenance.

[0184] Based on this structure, the estimated maintenance cost can be output to an external device.

[0185] (Note 16) The diagnostic device as described in any one of Notes 1 to 15, wherein the diagnostic information includes the amount of cost benefits resulting from performing maintenance based on the type of maintenance.

[0186] With this structure, cost-effective amounts can be output to external devices.

[0187] (Note 17) A diagnostic device as described in any of Notes 1 to 16, wherein the processing device outputs maintenance information to the terminal of the operator performing the maintenance of the wind power generation device.

[0188] With this structure, operators can perform maintenance on the wind power generation unit 20 without the need for users to process maintenance orders.

[0189] (Appendix 18) A diagnostic method includes: acquiring parameters for diagnosing a wind power generation device as acquisition parameters, and acquiring attribute information of the wind power generation device as acquisition attribute information; and using corresponding information, determining diagnostic information based on the acquisition parameters and the acquisition attribute information, and outputting the diagnostic information to an external device, wherein the corresponding information is information that corresponds a combination of the diagnostic parameters and the attribute information of the wind power generation device to the diagnostic information, and the diagnostic information includes at least one of a diagnostic result related to an anomaly of the wind power generation device and a maintenance type of the wind power generation device.

[0190] Label Explanation

[0191] 10 Management System, 20 Wind Power Generation Device, 30 Collection Device, 45 Wind Power Generation Unit, 50 User Terminal, 70 Maintenance Terminal, 100 Diagnostic 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 Diagnostic 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. A diagnostic device, characterized in that, include: An interface that obtains parameters for the diagnosis of the wind power generation device as acquisition parameters, and obtains the attribute information of the wind power generation device as acquisition attribute information; The memory stores corresponding information that combines diagnostic parameters of the wind power generation device and attribute information of the wind power generation device with diagnostic information, the diagnostic information including diagnostic results related to anomalies of the wind power generation device and at least one of the maintenance types of the wind power generation device. as well as A processing device that uses the corresponding information to determine the diagnostic information based on the acquired parameters and the acquired attribute information, and outputs the diagnostic information to an external device.

2. The diagnostic device as described in claim 1, characterized in that, The corresponding information is a database that associates multiple combinations with multiple diagnostic information items corresponding to each of the multiple combinations. The processing device performs a determination process for identifying candidates for the diagnostic information by referring to the database. The processing device determines the diagnostic information from the candidates of the diagnostic information.

3. The diagnostic device as described in claim 2, characterized in that, The processing device performs a predictive process that predicts at least one of the lifespan of the target component of the wind power generation device and the damage level of the target component. The processing device uses the prediction result of the prediction process as a search condition and refers to the database to perform the determination process.

4. The diagnostic device as described in claim 2 or 3, characterized in that, In the database, each of the plurality of diagnostic information corresponds to a degree of appropriateness representing the suitability of that diagnostic information. When the number of candidates for the diagnostic information is 2 or more, the processing device determines the diagnostic information from the candidates based on the appropriateness corresponding to the diagnostic information that is a candidate.

5. The diagnostic device as described in claim 4, characterized in that, After outputting the diagnostic information to an external device, the processing device allows the user terminal to receive appropriateness information related to the appropriateness of the diagnostic information. The processing device updates the suitability based on the suitability information.

6. The diagnostic device as described in claim 5, characterized in that, When the number of candidate diagnostic information is two or more, the processing device determines the diagnostic information with the highest appropriateness from the two or more candidates. The diagnostic information includes the diagnostic results and the maintenance type. The suitability information includes first information indicating whether maintenance of the maintenance type can be performed. If the first information indicates that the maintenance has not been performed, the processing device updates the appropriateness to the minimum value.

7. The diagnostic device as described in claim 5, characterized in that, When the suitability information meets the prescribed benchmarks, the processing device outputs suggested information related to the operation of the wind power generation device to the external device.

8. The diagnostic device as described in claim 2 or 3, characterized in that, When the number of candidates for the diagnostic information is 1, the processing device outputs the diagnostic information as the candidate.

9. The diagnostic device as described in claim 2 or 3, characterized in that, When the number of candidates for the diagnostic information is 0, the processing device performs a new determination process based on the acquisition parameters without using the acquired attribute information.

10. The diagnostic device as claimed in claim 9, characterized in that, Even if the new determination process has been performed and the number of candidate diagnostic information is still 0, the processing device is allowed to receive new diagnostic information. The processing device outputs the new diagnostic information to the external device.

11. The diagnostic device according to any one of claims 1 to 3, characterized in that, The diagnostic information includes an estimated cost for maintenance based on the type of maintenance.

12. The diagnostic device according to any one of claims 1 to 3, characterized in that, The diagnostic information includes the amount of cost benefits resulting from performing maintenance based on the type of maintenance.

13. The diagnostic device according to any one of claims 1 to 3, characterized in that, The processing device outputs maintenance information to the terminal of the operator performing the maintenance of the wind power generation device, indicating that the maintenance is to be performed.

14. A diagnostic method, characterized in that, include: The parameters used for the diagnosis of the wind power generation device are obtained as the acquisition parameters, and the attribute information of the wind power generation device is obtained as the acquisition attribute information. as well as Using the corresponding information, diagnostic information is determined based on the acquired parameters and the acquired attribute information, and the diagnostic information is output to an external device. The corresponding information corresponds the combination of the diagnostic parameters and attribute information of the wind power generation device to the diagnostic information. The diagnostic information includes at least one of the diagnostic results related to the abnormality of the wind power generation device and the maintenance type of the wind power generation device.

Images