WO2023065580A1 - 风电机组齿轮箱的故障诊断方法及装置 - Google Patents

风电机组齿轮箱的故障诊断方法及装置 Download PDF

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WO2023065580A1
WO2023065580A1 PCT/CN2022/077779 CN2022077779W WO2023065580A1 WO 2023065580 A1 WO2023065580 A1 WO 2023065580A1 CN 2022077779 W CN2022077779 W CN 2022077779W WO 2023065580 A1 WO2023065580 A1 WO 2023065580A1
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gearbox
parameters
sample
rule
diagnosis
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PCT/CN2022/077779
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English (en)
French (fr)
Inventor
王青天
王海明
张育钧
张燧
李小翔
曾谁飞
关建越
陈朝晖
杨永前
冯帆
任鑫
王�华
Original Assignee
中国华能集团清洁能源技术研究院有限公司
华能(浙江)能源开发有限公司清洁能源分公司
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Priority claimed from CN202111211995.2A external-priority patent/CN114036656B/zh
Application filed by 中国华能集团清洁能源技术研究院有限公司, 华能(浙江)能源开发有限公司清洁能源分公司 filed Critical 中国华能集团清洁能源技术研究院有限公司
Publication of WO2023065580A1 publication Critical patent/WO2023065580A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present application relates to the field of energy technology, and in particular to a fault diagnosis method and device for a gearbox of a wind turbine, a wind turbine, electronic equipment, and a storage medium.
  • Wind power generation has the advantages of renewable and environmental protection and has been widely used.
  • Wind turbine is an important part of wind power generation, which can convert wind energy into AC power. It is a large rotating equipment that operates under variable conditions.
  • Wind turbines are mainly divided into direct-drive units and doubly-fed units. The difference lies in whether there is a transmission process of the gearbox.
  • doubly-fed units occupy an important market share.
  • the gearboxes of doubly-fed units work under alternating loads for a long time state, and the whole system also has complex coupled vibrations, which makes the fault diagnosis of the gearbox extremely complicated.
  • the first purpose of this application is to propose a fault diagnosis method for wind turbine gearboxes, which can diagnose working condition parameters and gearbox input parameters based on machine learning models and rule models, and diagnose machine learning diagnosis results and rules based on rule sets.
  • the diagnosis results are comprehensively diagnosed to obtain the target diagnosis results, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the second purpose of the present application is to propose a fault diagnosis device for a gearbox of a wind turbine.
  • the third purpose of the present application is to propose a wind turbine.
  • the fourth object of the present application is to provide an electronic device.
  • the fifth object of the present application is to provide a computer-readable storage medium.
  • the embodiment of the first aspect of the present application proposes a fault diagnosis method for the gearbox of a wind turbine, including: collecting the operating parameters of the wind turbine, the operating parameters include working condition parameters and gearbox parameters; One of the parameters in the gearbox is used as an output parameter of the gearbox, and other parameters in the gearbox parameters are used as gearbox input parameters; the working condition parameters and the gearbox input parameters are input to the In the machine learning model corresponding to the output parameter of the gearbox, the predicted value of the output parameter of the gearbox is obtained; according to the predicted value of the output parameter of the gearbox and the output parameter of the gearbox, a machine learning diagnosis result is generated; Input the state parameters and the input parameters of the gearbox into the rule model corresponding to the output parameters of the gearbox to obtain the rule diagnosis result; perform comprehensive diagnosis according to the machine learning diagnosis result, the rule diagnosis result and the rule set to obtain the target diagnostic result.
  • the operating condition parameters and the gearbox input parameters can be diagnosed based on the machine learning model and the rule model, and the machine learning diagnosis result and the rule diagnosis result can be comprehensively diagnosed based on the rule set , to obtain the target diagnosis result, so that under the premise of lower cost, the accurate diagnosis of the gearbox will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the fault diagnosis method for the wind turbine gearbox according to the above-mentioned embodiments of the present application may also have the following additional technical features:
  • the method further includes: acquiring sample machine learning diagnosis results, sample rule diagnosis results, and sample target diagnosis results; A sample machine learning diagnosis result and the sample rule diagnosis result; performing a checksum test on the sample machine learning diagnosis result and the sample rule diagnosis result corresponding to different sample target diagnosis results to obtain the rule gather.
  • the method further includes: encoding the diagnosis result of the rule.
  • the method further includes: determining an operable range of the gearbox output parameters; quantifying the gearbox output parameters beyond the operable range to obtain the rule model.
  • the method further includes: quantifying the output parameter of the gearbox according to the overrun threshold of the output parameter of the gearbox provided by the manufacturer, to obtain the rule model.
  • the method further includes: quantifying the output parameters of the gearbox according to the degree of dispersion and fluctuation state of the output parameters of the gearbox at different time periods, to obtain the rule model.
  • the method further includes: calculating a correlation coefficient between a sample gearbox input parameter and the sample gearbox output parameter;
  • the gearbox input parameters are determined as the target sample gearbox input parameters.
  • the method further includes: determining a second preset number of sample gearbox input parameters with greater importance as the target sample gearbox input parameters.
  • the generating a machine learning diagnosis result according to the predicted value of the output parameter of the gearbox and the output parameter of the gearbox includes: calculating the predicted value of the output parameter of the gearbox and the output parameter of the gearbox The gearbox outputs a residual value between parameters; and the machine learning diagnosis result is generated according to the residual value and a residual value threshold.
  • the method further includes: calculating the mean value and standard deviation of the residual value during the machine learning model training process; calculating the residual value threshold according to the mean value and the standard deviation .
  • the embodiment of the second aspect of the present application proposes a fault diagnosis device for the gearbox of a wind turbine, including: a data acquisition module for collecting operating parameters of the wind turbine, the operating parameters include working condition parameters and gearbox parameters; determine A module for sequentially using one of the gearbox parameters as a gearbox output parameter, and using other parameters in the gearbox parameters except the one parameter as a gearbox input parameter; a prediction module for using The working condition parameters and the input parameters of the gearbox are input into the machine learning model corresponding to the output parameters of the gearbox to obtain the predicted value of the output parameters of the gearbox; The predicted value of the gearbox output parameter and the machine learning diagnosis result are generated; the rule diagnosis module is used to input the working condition parameter and the gearbox input parameter into the rule model corresponding to the gearbox output parameter, Obtaining a rule diagnosis result; a comprehensive diagnosis module, configured to perform a comprehensive diagnosis according to the machine learning diagnosis result, the rule diagnosis result and the rule set, to obtain a target diagnosis result.
  • the fault diagnosis device for the gearbox of a wind turbine in the embodiment of the present application can diagnose the working condition parameters and the input parameters of the gearbox based on the machine learning model and the rule model, and perform comprehensive diagnosis on the machine learning diagnosis result and the rule diagnosis result based on the rule set, Obtaining the target diagnosis results, so as to accurately diagnose the gearbox at a lower cost, will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • fault diagnosis device for the wind turbine gearbox may also have the following additional technical features:
  • the device further includes: a rule generation module, the rule generation module is used to: obtain sample machine learning diagnosis results, sample rule diagnosis results, and sample target diagnosis results; analyze methods based on association rules Determining the sample machine learning diagnostic results and the sample rule diagnostic results corresponding to different sample target diagnostic results; The rule diagnosis result is checked and tested to obtain the rule set.
  • a rule generation module the rule generation module is used to: obtain sample machine learning diagnosis results, sample rule diagnosis results, and sample target diagnosis results; analyze methods based on association rules Determining the sample machine learning diagnostic results and the sample rule diagnostic results corresponding to different sample target diagnostic results; The rule diagnosis result is checked and tested to obtain the rule set.
  • the rule diagnosis module is further configured to: encode the result of the rule diagnosis.
  • the device further includes: a quantization module, and the training module is used to: determine the operable range of the gearbox output parameters; The parameters are quantized to obtain the rule model.
  • the quantization module is further configured to: quantify the output parameter of the gearbox according to the overrun threshold of the output parameter of the gearbox provided by the manufacturer, so as to obtain the rule model.
  • the quantization module is further configured to: quantify the output parameters of the gearbox according to the degree of dispersion and the fluctuation state of the output parameters of the gearbox at different time periods, so as to obtain the rule model .
  • the device further includes: a training module, the training module includes: a determining unit, configured to determine a target sample gearbox input parameter according to a sample gearbox output parameter; a training unit, used to convert the sample Working condition parameters and the target sample gearbox input parameters are used as input, and the sample gearbox output parameters are used as output to train the machine learning model to be trained to obtain the machine learning model.
  • a training module includes: a determining unit, configured to determine a target sample gearbox input parameter according to a sample gearbox output parameter; a training unit, used to convert the sample Working condition parameters and the target sample gearbox input parameters are used as input, and the sample gearbox output parameters are used as output to train the machine learning model to be trained to obtain the machine learning model.
  • the determination unit is further configured to: calculate the correlation coefficient between the sample gearbox input parameter and the sample gearbox output parameter;
  • the sample gearbox input parameter is determined as the target sample gearbox input parameter.
  • the determining unit is further configured to: determine a second preset number of sample gearbox input parameters with greater importance as the target sample gearbox input parameters.
  • the diagnosis module is further used to: calculate the residual value between the predicted value of the output parameter of the gearbox and the output parameter of the gearbox; A difference threshold generates the machine learning diagnosis.
  • the diagnosis module is further used to: calculate the mean and standard deviation of the residual value during the training process of the machine learning model; calculate the residual value according to the mean and the standard deviation Difference threshold.
  • the embodiment of the third aspect of the present application provides a wind turbine, including: the fault diagnosis device for the gearbox of the wind turbine as described in the embodiment of the second aspect of the application.
  • the wind turbine in the embodiment of the present application can diagnose the operating condition parameters and gearbox input parameters based on the machine learning model and the rule model, and perform comprehensive diagnosis on the machine learning diagnosis result and the rule diagnosis result based on the rule set to obtain the target diagnosis result, thereby On the premise of lower cost, accurate diagnosis of the gearbox will effectively improve the reliability of wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the fourth aspect of the present application proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and operable on the processor.
  • the processor executes the program, it realizes the The method for diagnosing the fault of the wind turbine gearbox described in the embodiment of the first aspect.
  • the electronic equipment of the embodiment of the present application through the processor executing the computer program stored in the memory, can diagnose the working condition parameters and the gearbox input parameters based on the machine learning model and the rule model, and diagnose the machine learning diagnosis results and rules based on the rule set
  • the diagnosis results are comprehensively diagnosed to obtain the target diagnosis results, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the fifth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored.
  • the program is executed by a processor, the fault of the wind turbine gearbox as described in the embodiment of the first aspect of the present application is realized. diagnosis method.
  • the computer-readable storage medium of the embodiment of the present application stores computer programs and is executed by a processor, and can diagnose working condition parameters and gearbox input parameters based on machine learning models and rule models, and diagnose machine learning diagnosis results and gearbox input parameters based on rule sets. Based on the comprehensive diagnosis of the rule diagnosis results, the target diagnosis results can be obtained, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the sixth aspect of the present application provides a computer program product, the computer program product includes computer program code, when the computer program code is run on the computer, to execute the computer program described in the embodiment of the first aspect of the application Fault diagnosis method of wind turbine gearbox.
  • the embodiment of the seventh aspect of the present application provides a computer program, the computer program includes computer program code, and when the computer program code is run on the computer, the computer executes the computer program described in the embodiment of the first aspect of the present application. Fault diagnosis method of wind turbine gearbox.
  • FIG. 1 is a schematic flowchart of a fault diagnosis method for a wind turbine gearbox according to an embodiment of the present application
  • FIG. 2 is a schematic flow chart of the training process of the machine learning model in the fault diagnosis method for the gearbox of a wind turbine according to an embodiment of the present application;
  • Fig. 3 is a schematic flow chart of determining input parameters of a target sample gearbox in a fault diagnosis method for a gearbox of a wind turbine according to an embodiment of the present application;
  • Fig. 5 is a schematic flowchart of determining a residual value threshold in a fault diagnosis method for a wind turbine gearbox according to an example of the present application
  • FIG. 6 is a schematic diagram of generating a rule model in a fault diagnosis method for a wind turbine gearbox according to an example of the present application
  • FIG. 7 is a schematic flow diagram of generating a rule model based on an operating mechanism in a fault diagnosis method for a wind turbine gearbox according to an example of the present application
  • Fig. 8 is a schematic flowchart of generating a rule set in a fault diagnosis method for a gearbox of a wind turbine according to an embodiment of the present application
  • FIG. 9 is a schematic diagram of a scene of a fault diagnosis method for a wind turbine gearbox according to an example of the present application.
  • FIG. 10 is a schematic structural diagram of a fault diagnosis device for a wind turbine gearbox according to an embodiment of the present application.
  • Fig. 11 is a schematic structural diagram of a wind turbine according to an embodiment of the present application.
  • FIG. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • Fig. 1 is a schematic flowchart of a fault diagnosis method for a gearbox of a wind turbine according to an embodiment of the present application.
  • the fault diagnosis method of the wind turbine gearbox in the embodiment of the present application includes:
  • S101 collecting the operating parameters of the wind turbine, the operating parameters include working condition parameters and gearbox parameters.
  • the operating parameters of the wind turbines are collected, for example, the operating parameters of the wind turbines are acquired through a supervisory control and data acquisition (Scada) system.
  • Scada supervisory control and data acquisition
  • the operating parameters may include, but not limited to, operating condition parameters, gearbox parameters, and the like.
  • the working condition parameters can be power, speed, wind speed, and pitch angle, etc.
  • the gearbox parameters can be key characteristic parameters related to gearbox faults, such as gearbox oil temperature, gearbox inlet temperature, and gearbox bearing temperature.
  • the content contained in the operating parameters can be set according to needs, and this application does not make too many limitations.
  • preprocessing such as data cleaning can be performed on the collected operating parameters, such as eliminating shutdown data, power limit data, sensor abnormal data and power abnormal data, etc., to enhance the accuracy of fault diagnosis.
  • one of the multiple gearbox parameters collected is sequentially used as a gearbox output parameter, and other parameters among the above-mentioned multiple gearbox parameters except one parameter determined as a gearbox output parameter are used as The input parameters of the gearbox, that is, make each of the multiple collected parameters of the gearbox respectively serve as the output parameters of the gearbox for fault diagnosis in sequence.
  • the collected gearbox parameters include gearbox inlet temperature, gearbox oil temperature, gearbox bearing driving end temperature, gearbox bearing non-driving end temperature, gearbox inlet oil pressure, and gearbox outlet oil pressure.
  • the gearbox inlet temperature is used as the gearbox output parameter
  • the gearbox oil temperature, gearbox bearing driving end temperature, gearbox bearing non-driving end temperature, gearbox inlet oil pressure, and gearbox outlet oil pressure are used as gearbox input parameters.
  • the gearbox oil temperature is used as the gearbox output parameter
  • the gearbox inlet temperature, gearbox bearing driving end temperature, gearbox bearing non-driving end temperature, gearbox inlet oil pressure, and gearbox outlet oil pressure are used as gearbox Input parameters. Diagnosis of each gearbox parameter is realized in turn.
  • each gearbox output parameter may correspond to a machine learning model as a state feature.
  • obtain the machine learning model corresponding to the parameter and input the working condition parameter obtained in step S101 and the gearbox input parameter corresponding to the gearbox output parameter determined in step S102 into the obtained machine learning model, The predicted values of the output parameters of the gearbox are obtained.
  • the above process is repeated to complete the prediction of each gearbox output parameter determined in step S102 in order to obtain the corresponding predicted value.
  • abnormal self-diagnosis is performed according to the output parameters of the gearbox and the predicted values of the corresponding output parameters of the gearbox, and a machine learning diagnosis result is generated.
  • the training data used in the machine learning training is the data of the wind turbine in a fault-free state, it can be compared with the residual error of the output parameter of the gearbox to be diagnosed and the corresponding predicted value. Significant differences in residual values are used to generate machine learning diagnostics.
  • multiple residual value thresholds can be set according to the residual value in the training phase to perform different degrees of abnormal self-diagnosis.
  • the working condition parameters and the input parameters of the gearbox are input into the rule model corresponding to the output parameters of the gearbox, and the rule diagnosis is performed to obtain the rule diagnosis results, such as: oil temperature exceeds category 1 or oil temperature exceeds the limit Class 2 and other abnormal levels.
  • the rule diagnostic result output by the rule model can be coded, for example, the first type of overrun is coded as R001, that is, the text is characterized to be stored in the database, saving storage space.
  • the rule set may include rules expressed in the form of If-Then. Different rules represent different machine learning diagnosis results and the corresponding relationship between rule diagnosis results and failure modes. According to machine learning diagnosis results, rule diagnosis results and rule Aggregate for comprehensive diagnosis and get the target diagnosis result. For example:
  • the working condition parameters and the input parameters of the gearbox can be diagnosed based on the machine learning model and the rule model, and the machine learning diagnosis result and the rule diagnosis result can be analyzed based on the rule set.
  • Carrying out comprehensive diagnosis and obtaining the target diagnosis results, so as to accurately diagnose the gearbox under the premise of lower cost, will effectively improve the reliability of wind turbine operation and reduce the risk of damage to key components of the unit.
  • the fault diagnosis method of the wind turbine gearbox in the embodiment of the present application includes the training process of the machine learning model, which may specifically include the following steps:
  • the training of the machine learning model is carried out based on the full working condition data, the parameters of the sample gearbox parameters are analyzed except the sample gearbox output parameters, and the target sample gearbox input parameters are determined according to the sample gearbox output parameters , so that the determined target sample gearbox input parameters have a high correlation with the sample gearbox output parameters.
  • Method 1 Select multiple variable parameters from the working condition data, such as power, rotational speed and pitch angle, etc., and perform working condition clustering to obtain data groups under different working conditions, and perform data sampling in each working condition. Multiple sets of full working condition data are obtained as training data for machine learning.
  • working condition data such as power, rotational speed and pitch angle, etc.
  • Method 2 The working conditions can be divided into finer granularity according to the power, such as 0 ⁇ 100kW is a range corresponding to a working condition, sampling is carried out in each power range, and the data under each power range is obtained to form multiple groups of full working conditions training data.
  • one model can be used as the machine model to be trained, or a family of models can be selected as the machine model to be trained, and an optimal model can be selected from the family of models.
  • random forest model, neural network model and Xgboost model can be selected as a family of models, and the training data is divided into three parts: training set, verification set and test set, and the sample working condition parameters and target sample gearbox input parameters are used as Input, with sample gearbox output parameters as output, to train the machine learning model to be trained.
  • Candidate models use the test set to compare the performance of various candidate models, and then determine the best model, that is, determine a model in a family of models, such as the Xgboost model.
  • the trained model is persisted, such as saving the model to a hard disk, a storage device of a server, or the cloud, etc., to provide model preparation for fault diagnosis.
  • step S201 determining the input parameters of the target sample gearbox according to the output parameters of the sample gearbox.
  • the correlation coefficient between the remaining sample gearbox parameters (that is, the sample gearbox input parameters) and the sample gearbox output parameters can be calculated respectively, and the sample gearbox input parameters can be compared.
  • the correlation between the parameters and the output parameters of the sample gearbox, where the calculation formula of the correlation coefficient is as follows:
  • x represents the sample gearbox output parameters
  • y represents the sample gearbox input parameters
  • ⁇ represents the standard corresponding to the output parameters of the sample gearbox and the input parameters of the sample gearbox Difference.
  • the first predetermined number is selected among the multiple sample gearbox input parameters with relatively large correlation coefficients
  • the sample gearbox input parameters of which are determined as the target sample gearbox input parameters. For example, according to the correlation coefficient, the input parameters of the sample gearbox are sorted according to the correlation, and the first predetermined number of sample gearbox input parameters with larger correlation coefficients are selected from large to small, wherein the first predetermined number can be set according to needs.
  • the input parameters of the second preset number of sample gearboxes with greater importance can also be determined as the target gearbox Input parameters.
  • a random forest model can be used to construct a machine learning model of a certain sample gearbox output parameter and corresponding multiple sample gearbox input parameters, and by sorting the importance of multiple sample gearbox input parameters, select A second preset number of sample gearbox input parameters with greater importance are determined as target gearbox input parameters. Wherein, the second preset quantity can be set as required.
  • step S104 "generating machine learning diagnosis results according to the predicted value of the gearbox output parameters and the gearbox output parameters" may specifically include the following steps:
  • the predicted value of the output parameter of the gearbox is compared with the real value, and the residual value between the predicted value of the output parameter of the gearbox and the output parameter of the gearbox is calculated.
  • the larger the residual value the more the predicted result The greater the difference from the actual result.
  • the predicted value and the actual value of temperature can be calculated according to the following formula:
  • y i is the real value of the temperature
  • F(xi ) is the predicted value of the temperature by the machine learning model.
  • the residual value corresponding to the output parameter of the gearbox is compared with the residual value threshold to generate a machine learning diagnosis result.
  • the "residual value threshold" in step S403 can be obtained according to the following steps:
  • the mean mean and standard deviation ⁇ of the residuals between the gearbox output parameters and the corresponding predicted values during machine learning model training are calculated.
  • the sum of the mean mean and K times the standard deviation ⁇ (mean+k ⁇ ) is used as the residual value threshold to perform abnormal self-diagnosis and generate a machine learning diagnosis result.
  • the multiple K can be set according to needs, and is not limited in this application.
  • the embodiment of the present application also includes the generation process of the rule model, as shown in Figure 6, which can be obtained in three ways:
  • Method 1 Determine the operable domains of multiple gearbox output parameters based on the operating mechanism of the wind turbine to determine diagnostic rules to generate a rule model.
  • generating a rule model based on the operating mechanism of the wind turbine may specifically include the following steps :
  • the output parameters of the gearbox beyond the operable range are quantified, and different quantification intervals can be used to characterize various levels of abnormalities in the state of the gearbox, which can be used as the diagnostic rule of the rule model to output parameters for diagnosis.
  • the second way is to generate a rule model based on expert experience, that is, quantify the output parameters of the gearbox according to the overrun threshold of the output parameters of the gearbox provided by the manufacturer, so as to determine the diagnosis rules and obtain the rule model.
  • the temperature is quantified according to the temperature overrun threshold provided by the equipment manufacturer.
  • multiple different thresholds can be set according to the overrun threshold provided by the manufacturer, and then the temperature High and low risk quantification, such as:
  • x represents the parameter value of the temperature
  • a and b represent different temperature overrun thresholds.
  • the third way is to generate a rule model based on expert experience. It is also possible to quantify the output parameters of the gearbox according to the degree of dispersion and fluctuation of the output parameters of the gearbox in different periods of time, so as to determine the diagnosis rules and obtain the rule model.
  • the embodiment of the present application also includes the generation process of the "rule set" in step S106, which may specifically include the following steps:
  • the accumulated failure case set is used as the sample set, which includes sample operating condition parameters, sample gearbox parameters, and sample target diagnosis results, etc., and the above machine learning diagnosis is performed on the sample set based on the machine learning model to obtain the sample machine Learn the diagnosis result; perform the above rule diagnosis on the sample set based on the rule model, and obtain the sample rule diagnosis result.
  • sample machine learning diagnosis results can include oil temperature residual exceeding the limit and inlet temperature residual exceeding the limit. category, driving end or non-driving end bearing temperature residual overrun category, etc.
  • sample rule diagnosis results can include oil temperature overrun category, bearing temperature overrun category, oil pressure-oil temperature feasible range category, etc., which need to be based on different types
  • the association between the sample machine learning diagnosis results and sample rule diagnosis results and different sample target diagnosis results determines the gearbox failure modes corresponding to different types of sample machine learning diagnosis results and sample rule diagnosis results.
  • the sample machine learning diagnosis results and the sample rule diagnosis results can be uniformly identified as an item set X
  • the sample target diagnosis results in the failure case set can be represented as an item set Y
  • X and Y can be identified based on the association rule analysis method. Association to determine the item set X' corresponding to different sample target results, where the item set X' includes sample machine learning diagnosis results and sample rule diagnosis results corresponding to different sample target results.
  • domain experts verify the item set X′ including the sample machine learning diagnosis results and sample rule diagnosis results corresponding to different sample target diagnosis results to ensure the rationality of the generated rules. After passing the rationality verification , the corresponding relationship between different sample target diagnosis results and different sample machine learning diagnosis results and sample rule diagnosis results in item set X′ and item set Y can be expressed in the form of if X′ then Y.
  • Y is used as a candidate rule for accuracy testing, for example, verifying whether the accuracy rate of the candidate rule in the failure case set is stable, and testing the false positive rate of the candidate rule in the healthy data set.
  • the candidate rules and the existing rules can also be redundantly processed to obtain a rule set.
  • Fig. 9 is a schematic diagram of a scenario of a fault diagnosis method for a wind turbine gearbox according to an example of this application.
  • the fault diagnosis method may include an offline part and an online part , where the offline part is mainly used to extract knowledge from the fault case set through machine learning diagnosis and rule diagnosis, analyze the machine learning diagnosis results and rule diagnosis results based on expert experience, and generate a rule set; the online part is mainly used for rule set-based, Working condition parameters and gearbox parameters for gearbox fault diagnosis: input working condition parameters and gearbox parameters into machine learning model and rule model respectively, output machine learning diagnosis results and rule diagnosis results, and analyze machine learning diagnosis results and Based on the diagnosis results of the rules, comprehensive diagnosis is carried out to obtain the target diagnosis results.
  • the present application also proposes a fault diagnosis device for a gearbox of a wind turbine.
  • Fig. 10 is a schematic structural diagram of a fault diagnosis device for a gearbox of a wind turbine according to an embodiment of the present application.
  • the fault diagnosis device 1000 for the wind turbine gearbox of the embodiment of the present application includes: a data acquisition module 1001 , a determination module 1002 , a prediction module 1003 , a diagnosis module 1004 , a rule diagnosis module 1005 and a comprehensive diagnosis module 1006 .
  • the data collection module 1001 is used to collect the operating parameters of the wind turbine, and the operating parameters include working condition parameters and gearbox parameters;
  • the determining module 1002 is configured to sequentially use one of the gearbox parameters as a gearbox output parameter, and use other parameters except one of the gearbox parameters as gearbox input parameters.
  • the prediction module 1003 is configured to input the working condition parameters and the input parameters of the gearbox into the machine learning model corresponding to the output parameters of the gearbox to obtain the predicted value of the output parameters of the gearbox.
  • the diagnosis module 1004 is configured to generate a machine learning diagnosis result according to the predicted value of the output parameter of the gearbox and the output parameter of the gearbox.
  • the rule diagnosis module 1005 is used to input the operating condition parameters and the gearbox input parameters into the rule model corresponding to the gearbox output parameters to obtain the rule diagnosis result.
  • the comprehensive diagnosis module 1006 is configured to perform comprehensive diagnosis according to the machine learning diagnosis result, the rule diagnosis result and the rule set, and obtain the target diagnosis result.
  • the device further includes: a rule generation module, and the rule generation module is used to: obtain sample machine learning diagnosis results, sample rule diagnosis results, and sample target diagnosis results; The sample machine learning diagnosis results and sample rule diagnosis results corresponding to the diagnosis results; the checksum test is performed on the sample machine learning diagnosis results and sample rule diagnosis results corresponding to different sample target diagnosis results to obtain a rule set.
  • a rule generation module is used to: obtain sample machine learning diagnosis results, sample rule diagnosis results, and sample target diagnosis results; The sample machine learning diagnosis results and sample rule diagnosis results corresponding to the diagnosis results; the checksum test is performed on the sample machine learning diagnosis results and sample rule diagnosis results corresponding to different sample target diagnosis results to obtain a rule set.
  • the rule diagnosis module 1005 is further configured to: code the result of the rule diagnosis.
  • the quantization module is further configured to: quantify the output parameters of the gearbox according to the overrun threshold of the output parameters of the gearbox provided by the manufacturer to obtain a rule model.
  • the quantization module is further used to: quantify the output parameters of the gearbox according to the degree of dispersion and the fluctuation state of the output parameters of the gearbox at different time periods to obtain a rule model.
  • the device further includes: a training module, and the training module includes: a determination unit, used to determine the input parameters of the target sample gearbox according to the output parameters of the sample gearbox; a training unit, used to combine the sample working condition parameters and the target The input parameters of the sample gearbox are taken as input, and the output parameters of the sample gearbox are taken as output, and the machine learning model to be trained is trained to obtain the machine learning model.
  • the training module includes: a determination unit, used to determine the input parameters of the target sample gearbox according to the output parameters of the sample gearbox; a training unit, used to combine the sample working condition parameters and the target The input parameters of the sample gearbox are taken as input, and the output parameters of the sample gearbox are taken as output, and the machine learning model to be trained is trained to obtain the machine learning model.
  • the determination unit is further used to: calculate the correlation coefficient between the sample gearbox input parameters and the sample gearbox output parameters; determine the first preset number of sample gearbox input parameters with relatively large correlation coefficients as Target sample gearbox input parameters.
  • the determining unit is further configured to: determine a second preset number of sample gearbox input parameters with greater importance as target sample gearbox input parameters.
  • the diagnosis module 1004 is also used to: calculate the predicted value of the gearbox output parameter and the residual value between the gearbox output parameter; generate a machine learning diagnosis result according to the residual value and the residual value threshold .
  • the diagnostic module 1004 is further configured to: calculate the mean and standard deviation of the residual value during the machine learning model training process; calculate the residual value threshold according to the mean and standard deviation.
  • the fault diagnosis device for the wind turbine gearbox of the embodiment of the present application can diagnose the working condition parameters and the input parameters of the gearbox based on the machine learning model and the rule model, and can diagnose the machine learning diagnosis results and the rule diagnosis results based on the rule set.
  • Comprehensive diagnosis can obtain the target diagnosis results, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of wind turbine operation and reduce the risk of damage to key components of the unit.
  • the present application also proposes a wind turbine.
  • Fig. 11 is a schematic structural diagram of a wind turbine according to an embodiment of the present application.
  • a wind turbine 1100 includes the above-mentioned fault diagnosis device 1000 for a gearbox of a wind turbine.
  • the wind turbine in the embodiment of the present application can diagnose the operating condition parameters and gearbox input parameters based on the machine learning model and the rule model, and perform comprehensive diagnosis on the machine learning diagnosis result and the rule diagnosis result based on the rule set to obtain the target diagnosis result, thereby On the premise of lower cost, accurate diagnosis of the gearbox will effectively improve the reliability of wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the present application proposes an electronic device 1200, including: a memory 1201, a processor 1202, and a computer program stored in the memory 1201 and operable on the processor 1202, When the processor 1202 executes the program, the above-mentioned fault diagnosis method for the gearbox of the wind turbine is implemented.
  • the electronic equipment of the embodiment of the present application through the processor executing the computer program stored in the memory, can diagnose the working condition parameters and the gearbox input parameters based on the machine learning model and the rule model, and diagnose the machine learning diagnosis results and rules based on the rule set
  • the diagnosis results are comprehensively diagnosed to obtain the target diagnosis results, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the present application proposes a computer-readable storage medium on which a computer program is stored.
  • the program is executed by a processor, the above-mentioned method for fault diagnosis of a gearbox of a wind turbine is realized.
  • the computer-readable storage medium of the embodiment of the present application stores computer programs and is executed by a processor, and can diagnose working condition parameters and gearbox input parameters based on machine learning models and rule models, and diagnose machine learning diagnosis results and gearbox input parameters based on rule sets. Based on the comprehensive diagnosis of the rule diagnosis results, the target diagnosis results can be obtained, so that the gearbox can be accurately diagnosed at a lower cost, which will effectively improve the reliability of the wind turbine operation and reduce the risk of damage to key components of the unit.
  • the embodiment of the present application proposes a computer program product, the computer program product includes computer program code, when the computer program code is run on the computer, to execute the above-mentioned wind turbine gearbox Fault diagnosis method.
  • the embodiment of the present application proposes a computer program, the computer program includes computer program code, when the computer program code is run on the computer, so that the computer executes the above-mentioned failure of the wind turbine gearbox diagnosis method.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • “plurality” means two or more, unless otherwise specifically defined.

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Abstract

一种风电机组齿轮箱的故障诊断方法及装置。该方法包括：采集风电机组的运行参数；依次将齿轮箱参数中的一个参数作为齿轮箱输出参数，其他参数作为齿轮箱输入参数；将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的机器学习模型中，得到齿轮箱输出参数的预测值；根据齿轮箱输出参数的预测值和齿轮箱输出参数，生成机器学习诊断结果；将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的规则模型中，得到规则诊断结果；根据机器学习诊断结果、规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。

Description

风电机组齿轮箱的故障诊断方法及装置
相关申请的交叉引用
本申请基于申请号为No.202111211995.2、申请日为2021年10月18日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及能源技术领域，特别涉及一种风电机组齿轮箱的故障诊断方法、装置、风电机组、电子设备和存储介质。
背景技术
目前，随着能源短缺问题的加重，风能作为一种非常重要的清洁能源，将在未来的低碳时代中发挥不可替代的作用。风力发电具有可再生、环保等优点得到了越来越广泛的应用，而风电机组是风力发电的重要部件，可将风能转化为交流电能，是一种变工况运行的大型旋转设备。风电机组主要分为直驱机组与双馈机组，区别在于是否存在齿轮箱的变速过程，且在风能领域中，双馈机组占据重要的市场份额，双馈机组的齿轮箱长期工作在交变载荷状态下，且整个***还存在复杂的耦合振动，使得齿轮箱的故障诊断异常复杂。
因此，如何在较低成本的前提下，对齿轮箱进行准确诊断，有效提高风电机组运行的可靠性，降低机组关键部件损坏风险，成为风力发电领域亟待解决的问题。
发明内容
本申请的第一个目的在于提出一种风电机组齿轮箱的故障诊断方法，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
本申请的第二个目的在于提出一种风电机组齿轮箱的故障诊断装置。
本申请的第三个目的在于提出一种风电机组。
本申请的第四个目的在于提出一种电子设备。
本申请的第五个目的在于提出一种计算机可读存储介质。
本申请第一方面实施例提出了一种风电机组齿轮箱的故障诊断方法，包括：采集风电机组的运行参数，所述运行参数中包括工况参数和齿轮箱参数；依次将所述齿轮箱参数中的一个参数作为齿轮箱输出参数，将所述齿轮箱参数中除所述一个参数之外的其他参数作为齿轮箱输入参数；将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对 应的机器学习模型中，得到所述齿轮箱输出参数的预测值；根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果；将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的规则模型中，得到规则诊断结果；根据所述机器学习诊断结果、所述规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
根据本申请实施例的风电机组齿轮箱的故障诊断方法，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
另外，根据本申请上述实施例提出的风电机组齿轮箱的故障诊断方法还可以具有如下附加的技术特征：
在本申请的一个实施例中，所述方法还包括：获取样本机器学习诊断结果、样本规则诊断结果和样本目标诊断结果；基于关联规则分析方法确定与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果；对所述与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果，进行校验和测试得到所述规则集合。
在本申请的一个实施例中，所述方法还包括：对所述规则诊断结果进行编码。
在本申请的一个实施例中，所述方法还包括：确定所述齿轮箱输出参数的可运行域；对超出所述可运行域的所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述方法还包括：根据厂家提供的所述齿轮箱输出参数的超限阈值对所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述方法还包括：根据所述齿轮箱输出参数在不同时段的离散程度和波动状态，对所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述方法还包括：根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数；将样本工况参数和所述目标样本齿轮箱输入参数作为输入，将所述样本齿轮箱输出参数作为输出，对待训练的机器学习模型进行训练，得到所述机器学习模型。
在本申请的一个实施例中，所述方法还包括：计算样本齿轮箱输入参数与所述样本齿轮箱输出参数的相关系数；将所述相关系数较大的第一预设数量的所述样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
在本申请的一个实施例中，所述方法还包括：将重要性较大的第二预设数量的样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
在本申请的一个实施例中，所述根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果，包括：计算所述齿轮箱输出参数的预测值和所述齿轮箱输出参数之间的残差值；根据所述残差值和残差值阈值生成所述机器学习诊断结果。
在本申请的一个实施例中，所述方法还包括：计算所述机器学习模型训练过程中的残 差值的均值与标准差；根据所述均值和所述标准差计算所述残差值阈值。
本申请第二方面实施例提出了一种风电机组齿轮箱的故障诊断装置，包括：数据采集模块，用于采集风电机组的运行参数，所述运行参数中包括工况参数和齿轮箱参数；确定模块，用于依次将所述齿轮箱参数中的一个参数作为齿轮箱输出参数，将所述齿轮箱参数中除所述一个参数之外的其他参数作为齿轮箱输入参数；预测模块，用于将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的机器学习模型中，得到所述齿轮箱输出参数的预测值；诊断模块，用于根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果；规则诊断模块，用于将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的规则模型中，得到规则诊断结果；综合诊断模块，用于根据所述机器学习诊断结果、所述规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
本申请实施例的风电机组齿轮箱的故障诊断装置，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
另外，根据本申请上述实施例提出的风电机组齿轮箱的故障诊断装置还可以具有如下附加的技术特征：
在本申请的一个实施例中，所述装置，还包括：规则生成模块，所述规则生成模块用于：获取样本机器学习诊断结果、样本规则诊断结果和样本目标诊断结果；基于关联规则分析方法确定与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果；对所述与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果，进行校验和测试得到所述规则集合。
在本申请的一个实施例中，所述规则诊断模块，还用于：对所述规则诊断结果进行编码。
在本申请的一个实施例中，所述装置还包括：量化模块，所述训练模块用于：确定所述齿轮箱输出参数的可运行域；对超出所述可运行域的所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述量化模块，还用于：根据厂家提供的所述齿轮箱输出参数的超限阈值对所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述量化模块，还用于：根据所述齿轮箱输出参数在不同时段的离散程度和波动状态，对所述齿轮箱输出参数进行量化，得到所述规则模型。
在本申请的一个实施例中，所述装置还包括：训练模块，所述训练模块包括：确定单元，用于根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数；训练单元，用于将样本工况参数和所述目标样本齿轮箱输入参数作为输入，将所述样本齿轮箱输出参数作为输出， 对待训练的机器学习模型进行训练，得到所述机器学习模型。
在本申请的一个实施例中，所述确定单元，还用于：计算样本齿轮箱输入参数与所述样本齿轮箱输出参数的相关系数；将所述相关系数较大的第一预设数量的所述样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
在本申请的一个实施例中，所述确定单元，还用于：将重要性较大的第二预设数量的样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
在本申请的一个实施例中，所述诊断模块，还用于：计算所述齿轮箱输出参数的预测值和所述齿轮箱输出参数之间的残差值；根据所述残差值和残差值阈值生成所述机器学习诊断结果。
在本申请的一个实施例中，所述诊断模块，还用于：计算所述机器学习模型训练过程中的残差值的均值与标准差；根据所述均值和所述标准差计算所述残差值阈值。
本申请第三方面实施例提出了一种风电机组，包括：如本申请第二方面实施例所述的风电机组齿轮箱的故障诊断装置。
本申请实施例的风电机组，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
本申请第四方面实施例提出了一种电子设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时，实现如本申请第一方面实施例所述的风电机组齿轮箱的故障诊断方法。
本申请实施例的电子设备，通过处理器执行存储在存储器上的计算机程序，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
本申请第五方面实施例提出了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时，实现如本申请第一方面实施例所述的风电机组齿轮箱的故障诊断方法。
本申请实施例的计算机可读存储介质，通过存储计算机程序并被处理器执行，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
本申请第六方面实施例提出了一种计算机程序产品，所述计算机程序产品中包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以执行如本申请第一方面实施例所述的风电机组齿轮箱的故障诊断方法。
本申请第七方面实施例提出了一种计算机程序，所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以使得计算机执行如本申请第一方面实施例所述的风电机组齿轮箱的故障诊断方法。
附图说明
图1为根据本申请实施例的风电机组齿轮箱的故障诊断方法的流程示意图；
图2为根据本申请实施例的风电机组齿轮箱的故障诊断方法中机器学习模型的训练过程的流程示意图；
图3为根据本申请实施例的风电机组齿轮箱的故障诊断方法中确定目标样本齿轮箱输入参数的流程示意图；
图4为根据本申请实施例的风电机组齿轮箱的故障诊断方法中机器学习模型异常自诊断的流程示意图；
图5为根据本申请示例的风电机组齿轮箱的故障诊断方法中确定残差值阈值的流程示意图；
图6为根据本申请示例的风电机组齿轮箱的故障诊断方法中生成规则模型的示意图；
图7为根据本申请示例的风电机组齿轮箱的故障诊断方法中基于运行机理生成规则模型的流程示意图；
图8为根据本申请实施例的风电机组齿轮箱的故障诊断方法中生成规则集合的流程示意图；
图9为根据本申请示例的风电机组齿轮箱的故障诊断方法的场景示意图；
图10为根据本申请实施例的风电机组齿轮箱的故障诊断装置的结构示意图；
图11为根据本申请实施例的风电机组的结构示意图；
图12为根据本申请实施例的电子设备的结构示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
下面结合附图来描述本申请实施例的风电机组齿轮箱的故障诊断方法、装置、风电机组、电子设备和存储介质。
图1为根据本申请实施例的风电机组齿轮箱的故障诊断方法的流程示意图。
如图1所示，本申请实施例的风电机组齿轮箱的故障诊断方法，包括：
S101，采集风电机组的运行参数，运行参数中包括工况参数和齿轮箱参数。
本申请的实施例中，采集风电机组的运行参数，例如通过数据采集与监视控制*** (Supervisory Control And Data Acquisition，简称Scada)获取风电机组的运行参数。
在一些实施方式中，运行参数可以包括但不限于工况参数和齿轮箱参数等。例如，工况参数可以为功率、转速、风速和浆距角等，齿轮箱参数可以为齿轮箱油温、齿轮箱入口温度和齿轮箱轴承温度等与齿轮箱故障相关的关键特征参数。运行参数所包含的内容可根据需要设定，本申请不做过多限定。
在一些实施方式中，可以对采集到的运行参数进行数据清洗等预处理，例如剔除停机数据、限功率数据、传感器异常数据和功率异常数据等，以增强故障诊断的准确性。
S102，依次将齿轮箱参数中的一个参数作为齿轮箱输出参数，将齿轮箱参数中除一个参数之外的其他参数作为齿轮箱输入参数。
在一些实施方式中，依次将采集到的多个齿轮箱参数中的一个参数作为齿轮箱输出参数，将上述多个齿轮箱参数中除确定为齿轮箱输出参数的一个参数之外的其他参数作为齿轮箱输入参数，即：使得采集到的多个齿轮箱参数中的每一个参数分别作为齿轮箱输出参数依次进行故障诊断。
例如，采集到的齿轮箱参数中包括齿轮箱入口温度、齿轮箱油温、齿轮箱轴承驱动端温度、齿轮箱轴承非驱动端温度、齿轮箱入口油压、齿轮箱出口油压。将齿轮箱入口温度作为齿轮箱输出参数，将齿轮箱油温、齿轮箱轴承驱动端温度、齿轮箱轴承非驱动端温度、齿轮箱入口油压、齿轮箱出口油压作为齿轮箱输入参数。再一次的，将齿轮箱油温作为齿轮箱输出参数，将齿轮箱入口温度、齿轮箱轴承驱动端温度、齿轮箱轴承非驱动端温度、齿轮箱入口油压、齿轮箱出口油压作为齿轮箱输入参数。依次实现对每个齿轮箱参数的诊断。
S103，将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的机器学习模型中，得到齿轮箱输出参数的预测值。
本申请实施例中，每一个齿轮箱输出参数作为一个状态特征可以对应一个机器学习模型。针对一个齿轮箱输出参数，获取该参数对应的机器学习模型，将步骤S101获取的工况参数和步骤S102确定的与该齿轮箱输出参数对应的齿轮箱输入参数输入至获取的机器学习模型中，得到该齿轮箱输出参数的预测值。
在一些实施方式中，循环上述流程依次完成步骤S102确定的每一个齿轮箱输出参数的预测，得到对应的预测值。
在一些实施方式中，齿轮箱输出参数对应的机器学习模型可以从硬盘、服务器或云端获取，可以为预先训练好的机器学习模型。
S104，根据齿轮箱输出参数的预测值和齿轮箱输出参数，生成机器学习诊断结果。
本申请实施例中，根据齿轮箱输出参数和对应的齿轮箱输出参数的预测值进行异常自诊断，生成机器学习诊断结果。
作为一种可行的实施方式，由于在机器学习训练中采用的训练数据为风电机组无故障 状态的数据，可以通过对比待诊断的齿轮箱输出参数与对应的预测值的残差是否与训练阶段的残差值存在明显差异，以此生成机器学习诊断结果。
在一些实施方式中，可以根据训练阶段的残差值设置多个残差值阈值，进行不同程度的异常自诊断。
S105，将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的规则模型中，得到规则诊断结果。
本申请实施例中，将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的规则模型中，进行规则诊断，以得到规则诊断结果，比如：油温超限1类或油温超限2类等异常等级。
在一些实施方式中，可以对规则模型输出的规则诊断结果进行编码，比如将超限1类编码为R001，即将文字字符化以便存储于数据库中，节省存储空间。
S106，根据机器学习诊断结果、规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
本申请实施例中，规则集合中可以包括以If-Then形式表示的规则，不同规则表示不同机器学习诊断结果和规则诊断结果与故障模式的对应关系，根据机器学习诊断结果、规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。例如：
If：高转速工况and油温超限2类and驱动端轴承温度正常and油温波动异常1类and油压-油温可行域正常，
Then：齿轮箱散热***故障。
综上，根据本申请实施例的风电机组齿轮箱的故障诊断方法，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
在图1所示实施例的基础上，如图2所示，本申请实施例的风电机组齿轮箱的故障诊断方法话包括机器学习模型的训练过程，具体可包括以下步骤：
S201，根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数。
本申请实施例中，基于全工况数据进行机器学习模型的训练，对样本齿轮箱参数中除样本齿轮箱输出参数之外的参数进行分析，根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数，使得确定的目标样本齿轮箱输入参数与样本齿轮箱输出参数具有较高的相关性。
为进一步增强模型训练的准确性，可以在进行模型训练之前，对全工况数据进行筛选，例如可以通过以下两种方式进行筛选：
方式一：从工况数据中选取多个变量参数，如功率、转速和浆距角等数据，进行工况聚类以得到不同工况下的数据组，在每种工况下进行数据抽样，得到多组全工况数据，以此作为机器学习的训练数据。
方式二：可以根据功率进行更细粒度的工况划分，如0～100kW为一个区间对应一种工况，在每个功率区间进行抽样，得到各个功率区间下的数据，构成多组全工况训练数据。
S202，将样本工况参数和目标样本齿轮箱输入参数作为输入，将样本齿轮箱输出参数作为输出，对待训练的机器学习模型进行训练，得到机器学习模型。
本申请实施例中，可以使用一种模型作为待训练的机器模型，也可以选择一族模型作为待训练的机器模型，从一族模型中选择一个最优模型。
例如，可以选择随机森林模型、神经网络模型和Xgboost模型等作为一族模型，将训练数据划分为三份即：训练集、验证集和测试集，将样本工况参数和目标样本齿轮箱输入参数作为输入，将样本齿轮箱输出参数作为输出，对待训练的机器学习模型进行训练。例如，在训练集上分别训练多种模型，对每种模型进行超参数优化；使用验证集评价每个模型在不同超参数组合下的表现，从而确定最优参数，进而确定每一种模型对应的候选模型；利用测试集比较多种候选模型的性能，进而确定最佳模型，即确定一族模型中的某一种模型，如Xgboost模型。
在一些实施方式中，机器学习模型训练完成后，将训练好的模型进行模型持久化，例如将模型保存至硬盘、服务器的存储设备或云端等，为故障诊断提供模型准备。
在上述实施例的基础上，如图3所示，步骤S201中“根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数”可以包括以下步骤：
S301，计算样本齿轮箱输入参数与样本齿轮箱输出参数的相关系数。
本申请实施例中，针对每一个样本齿轮箱输出参数，可以通过分别计算剩余的其他样本齿轮箱参数(即样本齿轮箱输入参数)与该样本齿轮箱输出参数的相关系数，比较样本齿轮箱输入参数和样本齿轮箱输出参数之间的相关性，其中，相关系数计算公式如下：
Figure PCTCN2022077779-appb-000001
其中，x表示样本齿轮箱输出参数，y表示样本齿轮箱输入参数，
Figure PCTCN2022077779-appb-000002
表示样本齿轮箱输出参数对应的均值，
Figure PCTCN2022077779-appb-000003
表示样本齿轮箱输入参数对应的均值，Cov(x,y)为样本齿轮箱输出参数和样本齿轮箱输入参数对应的协方差，σ x为样本齿轮箱输出参数和样本齿轮箱输入参数对应的标准差。
S302，将相关系数较大的第一预设数量的样本齿轮箱输入参数确定为目标样本齿轮箱输入参数。
本申请实施例中，通过对某一特定的样本齿轮箱输出参数对应的每一个样本齿轮箱输入参数进行相关系数计算，在相关系数较大的多个样本齿轮箱输入参数中选取第一预定数量的样本齿轮箱输入参数，将其确定为目标样本齿轮箱输入参数。例如根据相关系数对样本齿轮箱输入参数进行相关性排序，从大到小选取相关系数较大的第一预定数量的样本齿 轮箱输入参数，其中第一预定数量可根据需要设定。
此外，除图3所示的目标样本齿轮箱输入参数的确定方法之外，本申请实施例中还可以通过将重要性较大的第二预设数量的样本齿轮箱输入参数确定为目标齿轮箱输入参数。
在一些实施方式中，可以采用随机森林模型等构造某一样本齿轮箱输出参数与对应的多个样本齿轮箱输入参数的机器学习模型，通过对多个样本齿轮箱输入参数进行重要性排序，选取重要性较大的第二预设数量的样本齿轮箱输入参数，将其确定为目标齿轮箱输入参数。其中，第二预设数量可根据需要设定。
在上述任一实施例的基础上，如图4所示，步骤S104中“根据齿轮箱输出参数的预测值和齿轮箱输出参数，生成机器学习诊断结果”具体可包括以下步骤：
S401，计算齿轮箱输出参数的预测值和齿轮箱输出参数之间的残差值。
本申请实施例中，对齿轮箱输出参数的预测值和真实值进行比较，计算出齿轮箱输出参数的预测值和齿轮箱输出参数之间的残差值，残差值越大则表示预测结果与实际结果差异越大。
例如，以温度作为当前待诊断的齿轮箱输出参数为例，可以将温度的预测值与真实值根据以下公式进行残差计算：
rec＝y i-F(x i)
其中，y i是温度的真实值，F(x i)是机器学习模型对温度的预测值。
S402，根据残差值和残差值阈值生成机器学习诊断结果。
本申请实施例中，将齿轮箱输出参数对应的残差值与残差值阈值进行比较，生成机器学习诊断结果。
在上述任一实施例的基础上，如图5所示，步骤S403中“残差值阈值”可根据以下步骤获得：
S501，计算机器学习模型训练过程中的残差值的均值与标准差。
在一些实施方式中，计算机器学习模型训练过程中齿轮箱输出参数与对应的预测值之间的残差的均值mean和标准差σ。
S502，根据均值和标准差计算残差值阈值。
在一些实施方式中，将均值mean与K倍标准差σ的和(mean+kσ)作为残差值阈值，进行异常自诊断，生成机器学习诊断结果。其中倍数K可根据需要设定，本申请不做限定。
在上述任一实施例的基础上，本申请实施例还包括规则模型的生成过程，如图6所示，可以通过三种方式获取：
方式一、基于风电机组的运行机理确定多个齿轮箱输出参数的可运行域，以确定诊断规则从而生成规则模型，如图7所示，基于风电机组的运行机理生成规则模型具体可包括 以下步骤：
S701，确定齿轮箱输出参数的可运行域。
本申请实施例中，根据齿轮箱输出参数对应的变化特征，确定齿轮箱输出参数的可运行域，即保证齿轮箱正常运行的齿轮箱输出参数的数据范围。
S702，对超出可运行域的齿轮箱输出参数进行量化，得到规则模型。
本申请实施例中，对超出可运行域的齿轮箱输出参数进行量化，不同的量化区间可以用于表征齿轮箱状态的多种等级的异常情况，以此作为规则模型的诊断规则对齿轮箱输出参数进行诊断。
方式二、基于专家经验生成规则模型，即根据厂家提供的齿轮箱输出参数的超限阈值对齿轮箱输出参数进行量化，以确定诊断规则得到规则模型。
例如，以温度作为齿轮箱输出参数为例，根据设备厂家提供的温度超限阈值对温度进行量化，在一些实施方式中，可以根据厂家提供的超限阈值设置多个不同的阈值，进而对温度高低进行风险量化，如：
Figure PCTCN2022077779-appb-000004
其中，x表示温度的参数值，a和b表示不同的温度超限阈值。
方式三、基于专家经验生成规则模型，还可以根据齿轮箱输出参数在不同时段的离散程度和波动状态，对齿轮箱输出参数进行量化，以确定诊断规则得到规则模型。
在上述任一实施例的基础上，如图8所示，本申请实施例还包括步骤S106中“规则集合”的生成过程，具体可包括以下步骤：
S801，获取样本机器学习诊断结果、样本规则诊断结果和样本目标诊断结果。
本申请实施例中，以积累的故障案例集作为样本集，其中包括样本工况参数、样本齿轮箱参数和样本目标诊断结果等，基于机器学习模型对样本集进行上述机器学习诊断，获取样本机器学习诊断结果；基于规则模型对样本集进行上述规则诊断，获取样本规则诊断结果。
S802，基于关联规则分析方法确定与不同样本目标诊断结果对应的样本机器学习诊断结果和样本规则诊断结果。
本申请实施例中，在故障案例集上获取的样本机器学习诊断结果和样本规则诊断结果存在多种类型，比如样本机器学习诊断结果可以有油温残差超限类、入口温度残差超限类、驱动端或非驱动端轴承温度残差超限类等；样本规则诊断结果可以有油温超限类、轴承温度超限类、油压-油温可行域类等，需要根据不同类型的样本机器学习诊断结果和样本规则诊断结果与不同样本目标诊断结果之间的关联，确定不同类型的样本机器学习诊断结果和样本规则诊断结果对应的齿轮箱故障模式。
在一些实施例中，可以将样本机器学习诊断结果和样本规则诊断结果统一标识为项集X，将故障案例集中的样本目标诊断结果表示为项集Y，将X与Y基于关联规则分析方法进行关联，以确定不同样本目标结果对应的项集X′，其中项集X′中包括与不同样本目标结果对应的样本机器学习诊断结果和样本规则诊断结果。
S803，对与不同样本目标诊断结果对应的样本机器学习诊断结果和样本规则诊断结果，进行校验和测试得到规则集合。
本申请实施例中，领域专家对包括不同样本目标诊断结果对应的样本机器学习诊断结果和样本规则诊断结果的项集X′进行校验，以确保生成规则的合理性，合理性校验通过后，可通过if X′ then Y的形式表示项集X′和项集Y中不同样本目标诊断结果与不同样本机器学习诊断结果和样本规则诊断结果的对应关系。
在一些实施方式中，将if X′ then Y作为候选规则，进行准确性测试，例如，验证候选规则在故障案例集中的准确率是否稳定，以及在健康数据集中测试候选规则的误报率。此外，为确保项集Y中的任一故障模式有且只有一条规则与之对应，还可以对候选规则与已有规则及进行冗余处理，以此获取规则集合。
为使本领域技术人员更清楚地了解本申请，图9为根据本申请示例的风电机组齿轮箱的故障诊断方法的场景示意图，如图9所示，该故障诊断方法可包括离线部分和在线部分，其中离线部分主要用于通过机器学习诊断、规则诊断对故障案例集进行知识提取，基于专家经验对机器学习诊断结果和规则诊断结果进行分析，生成规则集合；在线部分主要用于基于规则集合、工况参数和齿轮箱参数对齿轮箱进行故障诊断：将工况参数和齿轮箱参数分别输入机器学习模型和规则模型，输出机器学习诊断结果和规则诊断结果，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断获得目标诊断结果。
为了实现上述实施例，本申请还提出一种风电机组齿轮箱的故障诊断装置。
图10为根据本申请实施例的风电机组齿轮箱的故障诊断装置的结构示意图。
如图10所示，本申请实施例的风电机组齿轮箱的故障诊断装置1000，包括：数据采集模块1001、确定模块1002、预测模块1003、诊断模块1004、规则诊断模块1005和综合诊断模块1006。
数据采集模块1001，用于采集风电机组的运行参数，运行参数中包括工况参数和齿轮箱参数；
确定模块1002，用于依次将齿轮箱参数中的一个参数作为齿轮箱输出参数，将齿轮箱参数中除一个参数之外的其他参数作为齿轮箱输入参数。
预测模块1003，用于将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的机器学习模型中，得到齿轮箱输出参数的预测值。
诊断模块1004，用于根据齿轮箱输出参数的预测值和齿轮箱输出参数，生成机器学习诊断结果。
规则诊断模块1005，用于将工况参数和齿轮箱输入参数输入至齿轮箱输出参数对应的规则模型中，得到规则诊断结果。
综合诊断模块1006，用于根据机器学习诊断结果、规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
在本申请的实施例中，装置，还包括：规则生成模块，规则生成模块用于：获取样本机器学习诊断结果、样本规则诊断结果和样本目标诊断结果；基于关联规则分析方法确定与不同样本目标诊断结果对应的样本机器学习诊断结果和样本规则诊断结果；对与不同样本目标诊断结果对应的样本机器学习诊断结果和样本规则诊断结果，进行校验和测试得到规则集合。
在本申请的实施例中，规则诊断模块1005，还用于：对规则诊断结果进行编码。
在本申请的实施例中，装置还包括：量化模块，训练模块用于：确定齿轮箱输出参数的可运行域；对超出可运行域的齿轮箱输出参数进行量化，得到规则模型。
在本申请的实施例中，量化模块，还用于：根据厂家提供的齿轮箱输出参数的超限阈值对齿轮箱输出参数进行量化，得到规则模型。
在本申请的实施例中，量化模块，还用于：根据齿轮箱输出参数在不同时段的离散程度和波动状态，对齿轮箱输出参数进行量化，得到规则模型。
在本申请的实施例中，装置还包括：训练模块，训练模块包括：确定单元，用于根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数；训练单元，用于将样本工况参数和目标样本齿轮箱输入参数作为输入，将样本齿轮箱输出参数作为输出，对待训练的机器学习模型进行训练，得到机器学习模型。
在本申请的实施例中，确定单元，还用于：计算样本齿轮箱输入参数与样本齿轮箱输出参数的相关系数；将相关系数较大的第一预设数量的样本齿轮箱输入参数确定为目标样本齿轮箱输入参数。
在本申请的实施例中，确定单元，还用于：将重要性较大的第二预设数量的样本齿轮箱输入参数确定为目标样本齿轮箱输入参数。
在本申请的实施例中，诊断模块1004，还用于：计算齿轮箱输出参数的预测值和齿轮箱输出参数之间的残差值；根据残差值和残差值阈值生成机器学习诊断结果。
在本申请的实施例中，诊断模块1004，还用于：计算机器学习模型训练过程中的残差值的均值与标准差；根据均值和标准差计算残差值阈值。
需要说明的是，本申请实施例的风电机组齿轮箱的故障诊断装置中未披露的细节，请参照本申请实施例的风电机组齿轮箱的故障诊断方法中所披露的细节，这里不再赘述。
综上，本申请实施例的风电机组齿轮箱的故障诊断装置，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确 诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
为了实现上述实施例，本申请还提出一种风电机组。
图11为根据本申请实施例的风电机组的结构示意图。
如图11所示，本申请实施例的风电机组1100，包括上述的风电机组齿轮箱的故障诊断装置1000。
本申请实施例的风电机组，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
为了实现上述实施例，如图12所示，本申请实施例提出了一种电子设备1200，包括：存储器1201、处理器1202及存储在存储器1201上并可在处理器1202上运行的计算机程序，处理器1202执行程序时，实现上述的风电机组齿轮箱的故障诊断方法。
本申请实施例的电子设备，通过处理器执行存储在存储器上的计算机程序，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
为了实现上述实施例，本申请实施例提出了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时，实现上述的风电机组齿轮箱的故障诊断方法。
本申请实施例的计算机可读存储介质，通过存储计算机程序并被处理器执行，可基于机器学习模型和规则模型对工况参数和齿轮箱输入参数进行诊断，基于规则集合对机器学习诊断结果和规则诊断结果进行综合诊断，得到目标诊断结果，从而在较低成本的前提下，对齿轮箱进行准确诊断，将有效提高风电机组运行的可靠性，降低机组关键部件损坏风险。
为了实现上述实施例，本申请实施例提出了一种计算机程序产品，所述计算机程序产品中包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以执行上述的风电机组齿轮箱的故障诊断方法。
为了实现上述实施例，本申请实施例提出了一种计算机程序，所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以使得计算机执行上述的风电机组齿轮箱的故障诊断方法。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。在本申请的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
在本申请中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是机械 连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。
此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。

Claims (17)

  1. 一种风电机组齿轮箱的故障诊断方法，其特征在于，包括：
    采集风电机组的运行参数，所述运行参数中包括工况参数和齿轮箱参数；
    依次将所述齿轮箱参数中的一个参数作为齿轮箱输出参数，将所述齿轮箱参数中除所述一个参数之外的其他参数作为齿轮箱输入参数；
    将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的机器学习模型中，得到所述齿轮箱输出参数的预测值；
    根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果；
    将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的规则模型中，得到规则诊断结果；
    根据所述机器学习诊断结果、所述规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
  2. 根据权利要求1所述的故障诊断方法，其特征在于，还包括：
    获取样本机器学习诊断结果、样本规则诊断结果和样本目标诊断结果；
    基于关联规则分析方法确定与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果；
    对所述与不同所述样本目标诊断结果对应的所述样本机器学习诊断结果和所述样本规则诊断结果，进行校验和测试得到所述规则集合。
  3. 根据权利要求1或2所述的故障诊断方法，其特征在于，还包括：
    对所述规则诊断结果进行编码。
  4. 根据权利要求1至3中任一项所述的故障诊断方法，其特征在于，还包括：
    确定所述齿轮箱输出参数的可运行域；
    对超出所述可运行域的所述齿轮箱输出参数进行量化，得到所述规则模型。
  5. 根据权利要求1至4中任一项所述的故障诊断方法，其特征在于，还包括：
    根据厂家提供的所述齿轮箱输出参数的超限阈值对所述齿轮箱输出参数进行量化，得到所述规则模型。
  6. 根据权利要求1至5中任一项所述的故障诊断方法，其特征在于，还包括：
    根据所述齿轮箱输出参数在不同时段的离散程度和波动状态，对所述齿轮箱输出参数 进行量化，得到所述规则模型。
  7. 根据权利要求1至6中任一项所述的故障诊断方法，其特征在于，还包括：
    根据样本齿轮箱输出参数确定目标样本齿轮箱输入参数；
    将样本工况参数和所述目标样本齿轮箱输入参数作为输入，将所述样本齿轮箱输出参数作为输出，对待训练的机器学习模型进行训练，得到所述机器学习模型。
  8. 根据权利要求7所述的故障诊断方法，其特征在于，还包括：
    计算样本齿轮箱输入参数与所述样本齿轮箱输出参数的相关系数；
    将所述相关系数较大的第一预设数量的所述样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
  9. 根据权利要求7所述的故障诊断方法，其特征在于，还包括：
    将重要性较大的第二预设数量的样本齿轮箱输入参数确定为所述目标样本齿轮箱输入参数。
  10. 根据权利要求1至9中任一项所述的故障诊断方法，其特征在于，所述根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果，包括：
    计算所述齿轮箱输出参数的预测值和所述齿轮箱输出参数之间的残差值；
    根据所述残差值和残差值阈值生成所述机器学习诊断结果。
  11. 根据权利要求1至10中任一项所述的故障诊断方法，其特征在于，还包括：
    计算所述机器学习模型训练过程中的残差值的均值与标准差；
    根据所述均值和所述标准差计算所述残差值阈值。
  12. 一种风电机组齿轮箱的故障诊断装置，其特征在于，包括：
    数据采集模块，用于采集风电机组的运行参数，所述运行参数中包括工况参数和齿轮箱参数；
    确定模块，用于依次将所述齿轮箱参数中的一个参数作为齿轮箱输出参数，将所述齿轮箱参数中除所述一个参数之外的其他参数作为齿轮箱输入参数；
    预测模块，用于将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的机器学习模型中，得到所述齿轮箱输出参数的预测值；
    诊断模块，用于根据所述齿轮箱输出参数的预测值和所述齿轮箱输出参数，生成机器学习诊断结果；
    规则诊断模块，用于将所述工况参数和所述齿轮箱输入参数输入至所述齿轮箱输出参数对应的规则模型中，得到规则诊断结果；
    综合诊断模块，用于根据所述机器学习诊断结果、所述规则诊断结果和规则集合进行综合诊断，得到目标诊断结果。
  13. 一种风电机组，其特征在于，包括：如权利要求12所述的风电机组齿轮箱的故障诊断装置。
  14. 一种电子设备，其特征在于，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时，实现如权利要求1-11任一项所述的风电机组齿轮箱的故障诊断方法。
  15. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-11任一项所述的风电机组齿轮箱的故障诊断方法。
  16. 一种计算机程序产品，其特征在于，所述计算机程序产品中包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以执行如权利要求1-11中任一项所述的风电机组齿轮箱的故障诊断方法。
  17. 一种计算机程序，其特征在于，所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，以使得计算机执行如权利要求1-11中任一项所述的风电机组齿轮箱的故障诊断方法。
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