CN114400940B - Permanent magnet driving motor demagnetizing fault diagnosis method for electric automobile and electric automobile - Google Patents
Permanent magnet driving motor demagnetizing fault diagnosis method for electric automobile and electric automobile Download PDFInfo
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Abstract
The invention discloses a method for diagnosing demagnetization faults of a permanent magnet driving motor for an electric automobile and the electric automobile, wherein the method comprises the steps of collecting magnetic leakage signals of the permanent magnet driving motor for the electric automobile; converting the magnetic leakage signal into a two-dimensional wavelet time-frequency diagram through wavelet transformation; extracting self-coding features of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 1, and extracting features of a maximum stable extremum region of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 2; and inputting the feature vector 1 and the feature vector 2 into a pre-trained machine learning model to obtain a demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile. The invention can extract more effective fault high-dimensional characteristics, convert the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile into more visual image processing, and can improve the comprehensiveness and the accuracy of the fault high-dimensional characteristics and the accuracy of the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile through the fusion of the two types of characteristics.
Description
Technical Field
The invention relates to an electric automobile fault diagnosis technology, in particular to a permanent magnet driving motor demagnetizing fault diagnosis method for an electric automobile and the electric automobile.
Background
Since the major technological project of electric vehicles was started in 2001, the new energy vehicle industry in China has gone through the development process for 20 years, and China has become the production country and the consumption country of the largest new energy vehicles in the world, wherein the scale of the pure electric vehicles accounts for more than 50% of the world, and the pure electric vehicles are the first world and the overall level is internationally advanced. Along with the continuous improvement of the intellectualization and integration of new energy automobiles, the internal structure of the new energy automobiles is increasingly complex. In recent two years, the safety accident of the new energy automobile presents an increased situation, the industry is gradually changed from mileage anxiety into safety anxiety, and the safety problem becomes one of the core problems to be solved in the development of the new energy automobile.
The center of the power of the electric automobile is an electric motor. The permanent magnet driving motor adopts the permanent magnet to generate the motor magnetic field, has simple structure and high efficiency and control precision, and is widely applied to the fields of electric automobiles, aerospace, wind power generation and the like. The power of domestic and international electric automobiles is mostly provided by permanent magnet synchronous motors. Most of the magnetic steel sheets of the permanent magnet driving motor are made of neodymium iron boron permanent magnet materials, and the Curie temperature of the magnetic steel sheets is low. Therefore, overload of the motor, damage of a heat dissipation system and the like can cause magnetic loss of the permanent magnet, and demagnetization of the permanent magnet is extremely easy to occur. The demagnetizing faults can aggravate torque pulsation and motor loss, seriously reduce the performance of the automobile, and cause property loss and casualties when serious. Therefore, the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile has important practical significance.
There are few studies on the diagnosis of demagnetization faults of a permanent magnet drive motor for an electric vehicle, and particularly, there are few diagnostic methods for practical applications. Related researches are carried out, and only the diagnosis of the demagnetization faults of the permanent magnet motor without considering the application field can be referred to. The existing permanent magnet motor demagnetizing fault diagnosis method has achieved a series of results, and some results exist in both a data-driven diagnosis method and a model-based diagnosis method. For example, data-based diagnostic methods have mostly focused on fault diagnosis on intelligent algorithms or neural network algorithms. However, most data-driven diagnostic methods require a large amount of data to train the classifier, while model-based methods require the establishment of an accurate fault model. In addition, the existing method mainly focuses on fault diagnosis under one-dimensional signals, and does not consider rich fault high-dimensional characteristics. In the data-based demagnetization fault diagnosis, the diagnosis network under the convolutional neural network algorithm is generally deeper, and the calculation cost is high. However, in an actual electric vehicle application environment, the fault signal is susceptible to noise in a complex environment, resulting in difficulty in extracting fault features. Meanwhile, as the permanent magnet driving motor for the electric automobile is in a normal state for a long time, actual fault signals in different states are difficult to acquire. In addition, the complex working condition and the complex environment are the tests facing the practical application of the electric automobile, the fault signals obtained under the two conditions are complex, and the diagnosis effect of the traditional single-layer and single-layer diagnosis algorithm is poor. Therefore, how to realize the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile has become a key technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problems: aiming at the problems in the prior art, the invention provides a method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile and the electric automobile, which can extract more effective fault high-dimensional characteristics, convert the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile into more visual image processing, and can improve the comprehensiveness and the accuracy of the fault high-dimensional characteristics and the accuracy of the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile through the fusion of the two types of characteristics.
In order to solve the technical problems, the invention adopts the following technical scheme:
a permanent magnet driving motor demagnetizing fault diagnosis method for an electric automobile comprises the following steps:
1) Collecting a magnetic leakage signal of a permanent magnet driving motor for an electric automobile;
2) Converting the magnetic leakage signal into a two-dimensional wavelet time-frequency diagram through wavelet transformation;
3) Extracting self-coding features of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 1, and extracting features of a maximum stable extremum region of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 2;
4) And inputting the feature vector 1 and the feature vector 2 into a pre-trained machine learning model to obtain a demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile.
Optionally, in step 2), when the magnetic leakage signal is converted into the two-dimensional wavelet time-frequency chart through wavelet transformation, a functional expression of the wavelet transformation is shown as follows:
in the above formula, WT (alpha, tau) is the wavelet transformation result, alpha is the scale, tau is the translation amount, f (t) is the one-dimensional magnetic leakage signal,
is a mother wave function, t is time.
Optionally, in step 3), performing self-coding feature extraction on the two-dimensional wavelet time-frequency diagram to obtain a feature vector 1 includes: converting the two-dimensional wavelet time-frequency diagram into a gray-scale diagram, inputting the gray-scale diagram into a pre-trained double-layer self-encoder network, and extracting global features of the gray-scale diagram obtained after the conversion of the two-dimensional wavelet time-frequency diagram through the double-layer self-encoder network to serve as feature vectors 1; the double-layer self-encoder comprises two stacked self-encoders, wherein each self-encoder comprises an encoding layer and a decoding layer which are two network layers, the first self-encoder is used for extracting features to obtain shallow features, and the second self-encoder is used for extracting features of the shallow features to obtain depth features and taking the depth features as final obtained global features.
Optionally, in step 3), performing maximum stable extremum region feature extraction on the two-dimensional wavelet time-frequency diagram to obtain the feature vector 2 includes: sequentially binarizing the two-dimensional wavelet time-frequency diagram by using a group of binarization thresholds, and calculating a variable V (i) according to the following formula:
in the above, Q i Representing the area of the ith connected area in the binary area obtained by the current binary threshold, delta represents the preset change quantity of the binary threshold, and Q i+Δ Representing the area of the ith connected area in the binary area obtained by increasing the current binary threshold by a preset binary threshold change delta, Q i-Δ Representing the area of an ith connected area in a binary area obtained after the current binary threshold value is reduced by a preset binary threshold value change delta; if the variable V (i) is smaller than the set value, taking the binary region obtained by the current binary threshold value as the obtained maximum stable extremum region feature and converting the binary region into a vector as a feature vector 2.
Optionally, the machine learning model in step 4) includes a softmax classifier 1, a softmax classifier 2, and a comprehensive classifier, where the softmax classifier 1 is used for obtaining a classification probability value 1 according to the feature vector 1, the softmax classifier 2 is used for obtaining a classification probability value 2 according to the feature vector 2, and the comprehensive classifier is used for obtaining a final demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile according to the classification probability value 1 and the classification probability value 2.
Optionally, the integrated classifier is a support vector machine classifier.
Optionally, step 4) is preceded by the step of training the machine learning model with the data samples, and the step of training the machine learning model with the data samples includes the step of expanding the data samples with the generation countermeasure network:
s1) inputting a group of random numbers and corresponding sample labels into a generator for generating an countermeasure network, generating a virtual two-dimensional wavelet time-frequency diagram with the labels, and inputting a real two-dimensional wavelet time-frequency diagram with the labels and the virtual two-dimensional wavelet time-frequency diagram into a discriminator for generating the countermeasure network;
s2) respectively calculating the network loss and the network accuracy of the generator and the network loss and the network accuracy of the discriminator in the generated countermeasure network, and if one or all of the network loss and the network accuracy of the generator and the discriminator meet the requirements, judging that the training of the generated countermeasure network is completed, and jumping to the step S3); otherwise, adjusting parameters of a generator and a discriminator for generating the countermeasure network, and jumping to the step S1) to continue training to generate the countermeasure network;
s3) generating a virtual two-dimensional wavelet time-frequency diagram with labels by using a generator for generating an countermeasure network after training, wherein the virtual two-dimensional wavelet time-frequency diagram is used as a two-dimensional wavelet time-frequency diagram in a data sample.
Optionally, the functional expression of the network loss and the network accuracy of the generator in step S2) is:
in the above, L g Network loss of generator, S g The network accuracy of the generator, mean is the average calculation,
the probability that the virtual two-dimensional wavelet time-frequency diagram is identified as a real two-dimensional wavelet time-frequency diagram in the discriminator; the functional expression of the network loss and network accuracy of the arbiter in step S2) is:
in the above, L D For the loss of the discriminator network, S D For the network accuracy of the arbiter,
the probability of outputting the true two-dimensional wavelet time-frequency diagram in the discriminator network is +.>
The probability that the true two-dimensional wavelet time-frequency diagram is identified by the discriminator as a virtual two-dimensional wavelet time-frequency diagram.
In addition, the invention also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting the permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the permanent magnet driving motor demagnetization fault diagnosis method for the electric automobile.
In addition, the invention also provides a computer readable storage medium, which stores a computer program executed by a computer device to implement the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile
Compared with the prior art, the invention has the following advantages:
1. the method comprises the steps of collecting magnetic leakage signals of the permanent magnet driving motor for the electric automobile, converting the magnetic leakage signals into a two-dimensional wavelet time-frequency diagram through wavelet transformation, displaying more effective fault high-dimensional characteristics, and converting demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile into more visual image processing.
2. According to the invention, the two-dimensional wavelet time-frequency diagram is subjected to self-coding feature extraction to obtain the feature vector 1, the two-dimensional wavelet time-frequency diagram is subjected to maximum stable extremum region feature extraction to obtain the feature vector 2, and the comprehensiveness and accuracy of fault high-dimensional features can be improved through fusion of the two types of features, so that the accuracy of demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile can be improved.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
Fig. 2 shows two-dimensional wavelet time-frequency diagrams of normal motor and occurrence of different types of demagnetization faults.
Fig. 3 is a schematic diagram of extracting self-coding features from a two-dimensional wavelet time-frequency diagram to obtain a feature vector 1 according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of extracting self-coding features from a two-dimensional wavelet time-frequency diagram to obtain feature vectors 2 according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a softmax classifier in an embodiment of the invention.
Fig. 6 is a schematic structural diagram of a functional module of an electric automobile body according to an embodiment of the invention.
Detailed Description
Embodiment one:
as shown in fig. 1, the method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to the embodiment includes:
1) Collecting a magnetic leakage signal of a permanent magnet driving motor for an electric automobile;
2) Converting the magnetic leakage signal into a two-dimensional wavelet time-frequency diagram through wavelet transformation;
3) Extracting self-coding features of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 1, and extracting features of a maximum stable extremum region of the two-dimensional wavelet time-frequency diagram to obtain a feature vector 2;
4) And inputting the feature vector 1 and the feature vector 2 into a pre-trained machine learning model to obtain a demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile.
Fig. 2 shows two-dimensional wavelet time-frequency diagrams generated by measuring magnetic leakage signals on the surface of a motor by adopting a non-contact alternating-current magnetic sensor under 1000r/min for a normal motor and different types of demagnetization faults (namely, a demagnetization fault 1 and a demagnetization fault 2, wherein the demagnetization fault 1 is 30% and the demagnetization fault 2 is 100%), and referring to fig. 2, the two-dimensional wavelet time-frequency diagrams show more effective fault high-dimensional characteristics, and the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile can be converted into more visual image processing.
In this embodiment, when the magnetic flux leakage signal is converted into the two-dimensional wavelet time-frequency chart by wavelet transformation in step 2), the functional expression of the wavelet transformation is as follows:
in the above formula, WT (alpha, tau) is the wavelet transformation result, alpha is the scale, tau is the translation amount, f (t) is the one-dimensional magnetic leakage signal,
is a mother wave function, t is time.
In step 3) of this embodiment, performing self-coding feature extraction on the two-dimensional wavelet time-frequency diagram to obtain a feature vector 1 includes: converting the two-dimensional wavelet time-frequency diagram into a gray-scale diagram, inputting the gray-scale diagram into a pre-trained double-layer self-encoder network, and extracting global features of the gray-scale diagram obtained after the conversion of the two-dimensional wavelet time-frequency diagram through the double-layer self-encoder network to serve as feature vectors 1; the double-layer self-encoder comprises two stacked self-encoders, wherein each self-encoder comprises an encoding layer and a decoding layer which are two network layers, the first self-encoder is used for extracting features to obtain shallow features, and the second self-encoder is used for extracting features of the shallow features to obtain depth features and taking the depth features as final obtained global features. Fig. 3 is a schematic diagram of extracting self-coding features from a two-dimensional wavelet time-frequency diagram to obtain a feature vector 1. The double-layer self-encoder network is an existing neural network, and the required settings comprise maximum convolution times, L2 network weight regularization parameters, sparse regularization controller parameters, sparse regularization term parameters and telescopic data settings.
When the maximum stable extremum region characteristic in the image is extracted, the image sample is subjected to binarization processing by using a series of gray threshold values, then a corresponding binary region is obtained at each threshold value, and finally a region which can keep stable shape within a wide gray threshold value range is the maximum stable extremum region characteristic. In this embodiment, in step 3), performing maximum stable extremum region feature extraction on the two-dimensional wavelet time-frequency diagram to obtain the feature vector 2 includes: sequentially binarizing the two-dimensional wavelet time-frequency diagram by using a group of binarization thresholds, and calculating a variable V (i) according to the following formula:
in the above, Q i Representing the area of the ith connected area in the binary area obtained by the current binary threshold, delta represents the preset change quantity of the binary threshold, and Q i+Δ Representing the area of the ith connected area in the binary area obtained by increasing the current binary threshold by a preset binary threshold change delta, Q i-Δ Representing the area of an ith connected area in a binary area obtained after the current binary threshold value is reduced by a preset binary threshold value change delta; if the variable V (i) is smaller than the set value, taking the binary region obtained by the current binary threshold value as the obtained maximum stable extremum region feature and converting the binary region into a vector as a feature vector 2. Fig. 4 is a schematic diagram of extracting self-coding features from a two-dimensional wavelet time-frequency diagram to obtain feature vectors 2.
Referring to fig. 1, the machine learning model in step 4) of the present embodiment includes a softmax classifier 1, a softmax classifier 2, and a comprehensive classifier, where the softmax classifier 1 is used for obtaining a classification probability value 1 according to the feature vector 1, the softmax classifier 2 is used for obtaining a classification probability value 2 according to the feature vector 2, and the comprehensive classifier is used for obtaining a final demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile according to the classification probability value 1 and the classification probability value 2. Since the conventional softmax classifier may not perform well in some diagnostic data, the softmax classifier is improved in this embodiment, and is improved to an integrated classifier with n layers (n=1 to m, m is a positive integer) added after the conventional softmax classifier (softmax classifier 1, softmax classifier 2), and the diagnostic effect under complex working conditions and complex environments is improved by classifying again using the classification probability value output by the softmax.
The softmax classifier 1 and the softmax classifier 2 are both softmax neural networks, and the structures of the softmax neural networks are shown in figure 5. The softmax classifier 1 and the softmax classifier 2 calculate classification probability values based on a traditional softmax neural network, the sum of the classification probability values is 1, and the calculation function expression is as follows:
in the above formula, softmax represents the softmax classifier, y i Representing the softmax neural network classifier input, n representing the dimension of the softmax classifier. For multiple classifications, it is also necessary to determine how close the actual output is to the desired output by a cross entropy loss function, whose functional expression is:
in the above, loss i Represents a cross entropy loss function, t i Representing the true value, softmax represents the softmax classifier, y i Representing a softmax neural network classifier input.
It should be noted that the integrated classifier may employ a desired classifier, such as one or more layers of softmax classifier or other types of classifiers, etc., as desired. For example, as an alternative embodiment, the integrated classifier is a support vector machine classifier (SVM).
In this embodiment, step 4) further includes a step of training a machine learning model by using data samples, and the training of the softmax neural network and the support vector machine classifier (SVM) are well known methods, so they will not be described in detail herein.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile. Fig. 6 is a schematic structural diagram of a functional module of an electric automobile body according to an embodiment of the invention. Referring to fig. 6, in the whole system of the electric automobile, the whole automobile monitoring system issues a control command to the automobile driving control system through can communication, and the automobile driving control system controls the driving motor so that the electric automobile runs. For fault diagnosis, the whole monitoring system integrates fault diagnosis result display, and all algorithms and calculation processes of fault diagnosis are in the automobile driving control system. The magnetic flux sensor is used for measuring the magnetic flux leakage signal on the surface of the driving motor, the signal is transmitted to the automobile driving control system, and the automobile driving control system realizes the fault diagnosis of the motor based on the method of the embodiment and displays and outputs the diagnosis result in the whole automobile monitoring system.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
Embodiment two:
this embodiment is a further improvement over the first embodiment. In order to realize the demagnetization fault diagnosis of the permanent magnet driving motor for the electric automobile under a small sample, the method for training the machine learning model by adopting the data sample in the embodiment comprises the steps of expanding the data sample by adopting a generated countermeasure network:
s1) inputting a group of random numbers and corresponding sample labels into a generator for generating an countermeasure network, generating a virtual two-dimensional wavelet time-frequency diagram with the labels, and inputting a real two-dimensional wavelet time-frequency diagram with the labels and the virtual two-dimensional wavelet time-frequency diagram into a discriminator for generating the countermeasure network;
s2) calculating the network loss and the network accuracy of the discriminator, if the network loss and the network accuracy of the discriminator meet the requirements (the network loss is smaller than a set value, the network accuracy is larger than the set value), judging that the training of generating the countermeasure network is completed, and jumping to the step S3); otherwise, adjusting parameters of a generator and a discriminator for generating the countermeasure network, and jumping to the step S1) to continue training to generate the countermeasure network;
s3) generating a virtual two-dimensional wavelet time-frequency diagram with labels by using a generator for generating an countermeasure network after training, wherein the virtual two-dimensional wavelet time-frequency diagram is used as a two-dimensional wavelet time-frequency diagram in a data sample.
The condition generating countermeasure network is a deep neural network that is capable of generating data having the same characteristics as the true input data. One condition generating countermeasure network is constituted of two networks: a generator and a arbiter. The goal of the generator is to generate data that the arbiter recognizes as true. In order to maximize the probability that the arbiter recognizes the image generated by the generator as a true image, the negative log likelihood function is minimized. The virtual two-dimensional wavelet time-frequency diagram is generated by using the condition generation countermeasure network, so that the problem of few fault signals in the practical application of the electric automobile is solved.
The requirement of the arbiter is determined by a loss function and a network accuracy function, and the functional expressions of the network loss and the network accuracy of the arbiter in step S2) of the present embodiment are:
in the above, L D For the loss of the discriminator network, S D For the network accuracy of the arbiter,
the probability of outputting the true two-dimensional wavelet time-frequency diagram in the discriminator network is +.>
The probability that the true two-dimensional wavelet time-frequency diagram is identified by the discriminator as a virtual two-dimensional wavelet time-frequency diagram.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
Embodiment III:
the embodiment is basically the same as the second embodiment, and the main difference is that the iterative judgment conditions in the step S2) are different, and the step S2) is to calculate the network loss and the network accuracy of the generator, if the network loss and the network accuracy of the generator meet the requirements (the network loss is smaller than the set value, the network accuracy is greater than the set value), the training of generating the countermeasure network is judged to be completed, and the step S3) is skipped; otherwise, the parameters of the generator and the discriminator for generating the countermeasure network are adjusted, and the step S1) is skipped to continue training to generate the countermeasure network.
The requirements of the generator are determined by the network loss and the network accuracy of the generator, and the functional expressions of the network loss and the network accuracy of the generator in step S2) of the present embodiment are:
in the above, L g Network loss of generator, S g The network accuracy of the generator, mean is the average calculation,
the probability that a virtual two-dimensional wavelet time-frequency diagram is identified in the arbiter as a real two-dimensional wavelet time-frequency diagram.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
Embodiment four:
the present embodiment is basically the same as the second embodiment, and the main difference is that the iterative judgment conditions in step S2) are different, and the present embodiment S2) is the or logic operation of the first embodiment and the second embodiment, that is: calculating the network loss and the network accuracy of the generator and the network loss and the network accuracy of the discriminator in the generated countermeasure network respectively, and if one of the network loss and the network accuracy of the generator and the discriminator meet the requirements, judging that the training of the generated countermeasure network is completed, and skipping to the step S3); otherwise, the parameters of the generator and the arbiter for generating the countermeasure network are adjusted, the step S1) is skipped to continue training for generating the countermeasure network, and the training for generating the countermeasure network can be realized in the same way.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
Fifth embodiment:
the present embodiment is basically the same as the second embodiment, and the main difference is that the iterative judgment conditions in step S2) are different, and the present embodiment S2) is the and logic operation of the first embodiment and the second embodiment, that is: calculating the network loss and the network accuracy of the generator and the network loss and the network accuracy of the discriminator in the generated countermeasure network respectively, and if all the network losses and the network accuracy in the generator and the discriminator meet the requirements, judging that the training of the generated countermeasure network is completed, and skipping to the step S3); otherwise, the parameters of the generator and the arbiter for generating the countermeasure network are adjusted, the step S1) is skipped to continue training for generating the countermeasure network, and the training for generating the countermeasure network can be realized in the same way.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
Example six:
the present embodiment is basically the same as the second embodiment, and the main difference is that the iteration judgment conditions in the step S2) are different, and the step S2) is that if the iteration number is equal to the set value, the training of generating the countermeasure network is judged to be completed, and the step S3) is skipped; otherwise, the parameters of the generator and the arbiter for generating the countermeasure network are adjusted, the step S1) is skipped to continue training for generating the countermeasure network, and the training for generating the countermeasure network can be realized in the same way.
In addition, the embodiment also provides an electric automobile, which comprises an electric automobile body with a control unit and adopting a permanent magnet driving motor, wherein the control unit comprises a microprocessor and a memory which are connected with each other, the microprocessor is connected with a magnetic leakage signal sensor for collecting magnetic leakage signals, and the microprocessor is programmed or configured to execute the steps of the method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile.
In addition, the present embodiment also provides a computer-readable storage medium in which a computer program executed by a computer device to implement the foregoing method for diagnosing a demagnetization failure of a permanent magnet drive motor for an electric automobile is stored.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (9)
1. The method for diagnosing the demagnetization fault of the permanent magnet driving motor for the electric automobile is characterized by comprising the following steps of:
1) Collecting a magnetic leakage signal of a permanent magnet driving motor for an electric automobile;
2) Converting the magnetic leakage signal into a two-dimensional wavelet time-frequency diagram through wavelet transformation;
3) Carrying out self-coding feature extraction on the two-dimensional wavelet time-frequency diagram by using a double-layer self-coder network to obtain a feature vector 1, and carrying out maximum stable extremum region feature extraction on the two-dimensional wavelet time-frequency diagram to obtain a feature vector 2; the extracting the maximum stable extremum region feature to obtain the feature vector 2 includes: sequentially binarizing the two-dimensional wavelet time-frequency diagram by using a group of binarization thresholds, and calculating a variable V (i) according to the following formula:
in the above, Q i Representing the area of the ith connected area in the binary area obtained by the current binary threshold, delta represents the preset change quantity of the binary threshold, and Q i+Δ Representing the area of the ith connected area in the binary area obtained by increasing the current binary threshold by a preset binary threshold change delta, Q i-Δ Representing the area of an ith connected area in a binary area obtained after the current binary threshold value is reduced by a preset binary threshold value change delta; if the variable V (i) is smaller than the set value, taking the binary area obtained by the current binary threshold value as the obtained maximum stabilityDetermining the extreme value region characteristics and converting the extreme value region characteristics into vectors serving as characteristic vectors 2;
4) And inputting the feature vector 1 and the feature vector 2 into a pre-trained machine learning model to obtain a demagnetization fault diagnosis result of the permanent magnet driving motor for the electric automobile.
2. The method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to claim 1, wherein in the step 2), when the leakage magnetic signal is converted into a two-dimensional wavelet time-frequency diagram by wavelet transformation, a functional expression of the wavelet transformation is as follows:
in the above formula, WT (alpha, tau) is the wavelet transformation result, alpha is the scale, tau is the translation amount, f (t) is the one-dimensional magnetic leakage signal,
is a mother wave function, t is time.
3. The method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to claim 1, wherein the extracting the self-coding feature of the two-dimensional wavelet time-frequency diagram in step 3) to obtain the feature vector 1 includes: converting the two-dimensional wavelet time-frequency diagram into a gray-scale diagram, inputting the gray-scale diagram into a pre-trained double-layer self-encoder network, and extracting global features of the gray-scale diagram obtained after the conversion of the two-dimensional wavelet time-frequency diagram through the double-layer self-encoder network to serve as feature vectors 1; the double-layer self-encoder comprises two stacked self-encoders, wherein each self-encoder comprises an encoding layer and a decoding layer which are two network layers, the first self-encoder is used for extracting features to obtain shallow features, and the second self-encoder is used for extracting features of the shallow features to obtain depth features and taking the depth features as final obtained global features.
4. The method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to claim 1, wherein the machine learning model in the step 4) includes a softmax classifier 1, a softmax classifier 2 and a comprehensive classifier, the softmax classifier 1 is used for obtaining a classification probability value 1 according to the feature vector 1, the softmax classifier 2 is used for obtaining a classification probability value 2 according to the feature vector 2, and the comprehensive classifier is used for obtaining a final demagnetization fault diagnosis result of the permanent magnet drive motor for the electric automobile according to the classification probability value 1 and the classification probability value 2.
5. The method for diagnosing a demagnetization fault in a permanent magnet drive motor for an electric automobile according to claim 4, wherein the comprehensive classifier is a support vector machine classifier.
6. The method for diagnosing a demagnetization fault in a permanent magnet drive motor for an electric automobile according to claim 1, wherein the step 4) is preceded by a step of training a machine learning model using data samples, and the step of performing data sample expansion using a generation countermeasure network when training the machine learning model using the data samples is included:
s1) inputting a group of random numbers and corresponding sample labels into a generator for generating an countermeasure network, generating a virtual two-dimensional wavelet time-frequency diagram with the labels, and inputting a real two-dimensional wavelet time-frequency diagram with the labels and the virtual two-dimensional wavelet time-frequency diagram into a discriminator for generating the countermeasure network;
s2) respectively calculating the network loss and the network accuracy of the generator and the network loss and the network accuracy of the discriminator in the generated countermeasure network, and if one or all of the network loss and the network accuracy of the generator and the discriminator meet the requirements, judging that the training of the generated countermeasure network is completed, and jumping to the step S3); otherwise, adjusting parameters of a generator and a discriminator for generating the countermeasure network, and jumping to the step S1) to continue training to generate the countermeasure network;
s3) generating a virtual two-dimensional wavelet time-frequency diagram with labels by using a generator for generating an countermeasure network after training, wherein the virtual two-dimensional wavelet time-frequency diagram is used as a two-dimensional wavelet time-frequency diagram in a data sample.
7. The method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to claim 6, wherein the functional expression of the network loss and the network accuracy of the generator in step S2) is:
in the above, L g Network loss of generator, S g The network accuracy of the generator, mean is the average calculation,
the probability that the virtual two-dimensional wavelet time-frequency diagram is identified as a real two-dimensional wavelet time-frequency diagram in the discriminator; the functional expression of the network loss and network accuracy of the arbiter in step S2) is:
in the above, L D For the loss of the discriminator network, S D For the network accuracy of the arbiter,
the probability of outputting the true two-dimensional wavelet time-frequency diagram in the discriminator network is +.>
Is distinguished for a true two-dimensional wavelet time-frequency diagramThe probability of the identified virtual two-dimensional wavelet time-frequency graph.
8. An electric vehicle comprising an electric vehicle body with a control unit and employing a permanent magnet drive motor, the control unit comprising a microprocessor and a memory connected to each other, the microprocessor being connected to a leakage signal sensor for collecting leakage signals, characterized in that the microprocessor is programmed or configured to perform the steps of the method for diagnosing a demagnetization fault of the permanent magnet drive motor for an electric vehicle according to any of claims 1-7.
9. A computer-readable storage medium, wherein a computer program executed by a computer device to implement the method for diagnosing a demagnetization fault of a permanent magnet drive motor for an electric automobile according to any of claims 1 to 7 is stored in the computer-readable storage medium.
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