WO2023065580A1 - Procédé et appareil de diagnostic de défaut pour boîte de vitesses d'un groupe électrogène d'éolienne - Google Patents

Procédé et appareil de diagnostic de défaut pour boîte de vitesses d'un groupe électrogène d'éolienne 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)
Chinese (zh)
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
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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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Publication of WO2023065580A1 publication Critical patent/WO2023065580A1/fr

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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

L'invention concerne un procédé et un appareil de diagnostic de défaillance pour une boîte de vitesses d'un groupe électrogène d'éolienne. Le procédé consiste à : collecter des paramètres de fonctionnement d'un groupe électrogène d'éolienne ; prendre en séquence un paramètre de boîte de vitesses en tant que paramètre de sortie de boîte de vitesses, et prendre les autres paramètres en tant que paramètres d'entrée de boîte de vitesses ; entrer un paramètre de condition de travail et les paramètres d'entrée de boîte de vitesses dans un modèle d'apprentissage machine correspondant au paramètre de sortie de boîte de vitesses, de manière à obtenir une valeur prédite du paramètre de sortie de boîte de vitesses ; générer un résultat de diagnostic d'apprentissage machine en fonction de la valeur prédite du paramètre de sortie de boîte de vitesses et du paramètre de sortie de boîte de vitesses ; entrer le paramètre de condition de travail et les paramètres d'entrée de boîte de vitesses dans un modèle de règle correspondant au paramètre de boîte de vitesses de sortie, de façon à obtenir un résultat de diagnostic de règle ; et réaliser un diagnostic complet selon le résultat de diagnostic d'apprentissage machine, le résultat de diagnostic de règle et un ensemble de règles, de façon à obtenir un résultat de diagnostic cible.
PCT/CN2022/077779 2021-10-18 2022-02-24 Procédé et appareil de diagnostic de défaut pour boîte de vitesses d'un groupe électrogène d'éolienne WO2023065580A1 (fr)

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