CN112683532B - Cross-working condition countermeasure diagnostic method for bearing - Google Patents

Cross-working condition countermeasure diagnostic method for bearing Download PDF

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CN112683532B
CN112683532B CN202011342399.3A CN202011342399A CN112683532B CN 112683532 B CN112683532 B CN 112683532B CN 202011342399 A CN202011342399 A CN 202011342399A CN 112683532 B CN112683532 B CN 112683532B
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CN112683532A (en
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张兴武
张启旸
刘一龙
孙闯
李明
陈雪峰
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Xian Jiaotong University
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Abstract

The invention discloses a cross-working condition countermeasure diagnostic method of a bearing, which comprises the steps of collecting vibration data of the bearing in an operating state, dividing the vibration data to generate signal samples, and dividing the samples into a test set for testing and a training set for training; constructing a training module which comprises a feature extractor for extracting signal features, a classifier for classifying bearing faults and a discriminator for distinguishing the working conditions and faults of the features; the training module is trained on the basis of a training set, wherein the feature extraction parameters and the classifier parameters are updated according to a loss function by using a BP (back propagation) method; fixing the characteristic extractor parameters, and updating discriminator parameters by using the loss function; fixing discriminator parameters, and updating the characteristic extractor parameters by using a countermeasure loss function; and constructing a test module based on the updated classifier and the updated feature extractor, and inputting the test set and/or the target domain working condition sample into the test module to carry out fault diagnosis.

Description

Cross-working condition countermeasure diagnostic method for bearing
Technical Field
The invention belongs to the technical field of intelligent diagnosis of bearing faults, and particularly relates to a cross-working condition countermeasure diagnosis method for a bearing.
Background
The rolling bearing is an important key part in the rotating machinery, and whether the inside bearing is safe or not is directly related to whether the machinery can normally run or not. The traditional intelligent diagnosis relies on expert to extract features and complex signal processing, and the traditional method cannot be widely applied due to high dependence on expert knowledge.
The traditional artificial intelligence fault diagnosis method based on deep learning depends on a new working condition field sample to participate in training in the field of variable working conditions (the rotating speed and the load of a training set are different from the rotating speed or the load of a testing set), retraining is needed when the new working conditions are generated in actual operation, and a large amount of time and resources are wasted.
In the wheel set bearing fault diagnosis method based on the equal-weight local feature sparse filter network of the Western-Ann traffic university, firstly, vibration signals of a vibration bearing in different health states are utilized to establish a fault diagnosis model based on the equal-weight local feature sparse filter network, then the sparse filter network is trained, fault features are automatically extracted from the vibration signals by utilizing the trained sparse filter network, and finally, a Softmax classifier is trained based on the extracted fault features to perform fault diagnosis on the bearing. The method relies on expert knowledge and utilizes a complex signal processing method to extract screening characteristics, and a classifier is adopted to classify faults based on the extracted characteristics. The method needs manual feature extraction, and the quality of feature extraction particularly depends on the experience of experts and a signal processing method, so that certain obstruction is caused to the application of the technology.
In a bearing fault diagnosis method based on a deep countermeasure migration network of Shandong science and technology university, firstly, vibration signals of a bearing under different working conditions (different rotating speeds and loads) are obtained, a frequency spectrum signal is obtained through Fourier transform, data with a label under a certain working condition is used as source domain data, and data without labels under other working conditions is used as target domain data; two generators are used as a feature extraction network of a source domain and a target domain, Softmax cross entropy is used as a fault classifier, a discriminator is used as a domain discrimination network, a gradient inversion layer is added for domain discrimination training, and the performance of the feature extraction network is improved. Deep learning has great advantages in data mining and feature extraction, and can solve the problem of dependence on expert experience, but when the working condition changes, a semi-supervised method based on transfer learning is required to be adopted to be trained by combining new working condition label-free fault data. This method has a fatal disadvantage that when a new working condition is met, the training needs to be carried out again by combining the working condition data of the source domain again to adapt to the new working condition. However, this wastes a lot of time and resources, and it is difficult to obtain new condition fault data in some cases.
In the actual mechanical operation, the method adopted by the method has lower reliability and accuracy and cannot be quickly adapted to new working conditions. Especially under the environment of complex and changeable working conditions, the method for extracting the features needs to be continuously adjusted and the model needs to be continuously retrained. These shortcomings have led to the use of intelligent diagnostic methods in the field of bearing fault diagnosis.
The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a cross-working condition countermeasure diagnosis method for a bearing, which does not need to manually extract features to reduce the dependence on expert knowledge, and overcomes the defect of the traditional artificial intelligence semi-supervised variable working condition training, namely, data of a target field without labels are needed for training, and retraining is needed once the target field is changed. The method utilizes improved conditions to resist a training method, trains invariant features in the model learning field, and gets rid of the interference of working condition information on the model. Thus, it may perform well in the target domain.
The invention aims to realize the purpose through the following technical scheme, and the cross-working condition countermeasure diagnosis method of the bearing comprises the following steps:
the method comprises the steps that in the first step, vibration data of a rolling bearing under a plurality of different working conditions are collected, wherein the working conditions are divided into a source domain working condition and a target domain working condition; dividing the vibration data under the working condition of the source domain to generate a first signal sample, and taking out the signal sample according to a preset proportion to be used as a training set for training; dividing the vibration data under the working condition of the target domain to generate a second signal sample which is then used as a test set sample for testing;
in the second step, a training module is constructed, wherein the training module comprises a feature extractor for extracting signal features, a classifier for classifying bearing faults and a discriminator for distinguishing the working conditions and faults of the features, the feature extractor is provided with feature extraction parameters, the classifier is provided with classifier parameters, and the discriminator is provided with discriminator parameters;
in a third step, training the training module based on a training set, comprising:
updating the feature extraction parameters and the classifier parameters according to a loss function by using a BP method;
fixing the characteristic extractor parameters, and updating discriminator parameters by using the loss function;
fixing discriminator parameters, and updating the characteristic extractor parameters by using a countermeasure loss function;
and in the fourth step, a test module is constructed based on the updated classifier and the updated feature extractor, and a test set sample is input into the test module for fault diagnosis.
In the method, in the first step, the vibration data is divided to generate signal samples with the length of 1024 data points, and a test set and a training set for testing are randomly divided according to a preset proportion, wherein each signal sample from the test set and each signal sample from the training set are subjected to normalization processing,
Figure 237231DEST_PATH_IMAGE001
，
Figure 147418DEST_PATH_IMAGE002
are samples of the signal generated by the vibration data,
Figure 885698DEST_PATH_IMAGE003
is that
Figure 172323DEST_PATH_IMAGE002
The average value of (a) of (b),
Figure 312449DEST_PATH_IMAGE004
is that
Figure 675428DEST_PATH_IMAGE002
Standard deviation of (2).
In the method, in the second step, the training set is a source domain data set with known fault labels and working condition information
Figure 150272DEST_PATH_IMAGE005
，
Figure 979601DEST_PATH_IMAGE006
Wherein the source domainData set
Figure 223501DEST_PATH_IMAGE005
Is provided with
Figure 491802DEST_PATH_IMAGE007
A sample,
Figure 453942DEST_PATH_IMAGE008
Class and
Figure 98681DEST_PATH_IMAGE009
the working conditions of the individual source areas are set,
Figure 197087DEST_PATH_IMAGE010
the working condition serial number of the source domain;
Figure 636290DEST_PATH_IMAGE011
is the ith sample, which is from the ith
Figure 85726DEST_PATH_IMAGE010
The working condition of the source region belongs to
Figure 2997DEST_PATH_IMAGE012
Class, which is labeled as
Figure 221489DEST_PATH_IMAGE013
To a
Figure 831593DEST_PATH_IMAGE014
Discriminator label
Figure 768325DEST_PATH_IMAGE015
The expression is as follows: ,
Figure 489287DEST_PATH_IMAGE016
for the
Figure 827865DEST_PATH_IMAGE017
Classifier (A)
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) The output is:
Figure 783631DEST_PATH_IMAGE019
,
wherein
Figure 557552DEST_PATH_IMAGE020
,
Figure 501368DEST_PATH_IMAGE021
Is that the sample belongs to
Figure 968121DEST_PATH_IMAGE022
Class, discriminator actual output is:
Figure 364599DEST_PATH_IMAGE023
,
wherein
Figure 676631DEST_PATH_IMAGE024
Wherein, generation
Figure 740534DEST_PATH_IMAGE025
Watch sample
Figure 112609DEST_PATH_IMAGE026
Belong to the first
Figure 996383DEST_PATH_IMAGE010
Second of the individual domain
Figure 112106DEST_PATH_IMAGE012
Probability of individual class, the ideal output of the discriminator required by the feature extractor is:
Figure 30515DEST_PATH_IMAGE027
，
Figure 324224DEST_PATH_IMAGE028
representing samples at ideal output of discriminator
Figure 678982DEST_PATH_IMAGE029
Belong to the first
Figure 349129DEST_PATH_IMAGE010
Second of the individual domain
Figure 105732DEST_PATH_IMAGE012
Probability of individual class.
In the method, in the third step, the cross entropy is taken as a loss function, the BP method is used for updating the feature extraction parameters and the classifier parameters according to the loss function, the loss function is the cross entropy, and the expression is
Figure 847641DEST_PATH_IMAGE030
。
In the method, in the third step, the characteristic extractor parameter is fixed, and the discriminator parameter is updated by using the loss function, wherein the loss function expression is
Figure 955274DEST_PATH_IMAGE031
。
In the method, in the third step, the loss function is based on mean square error and has the expression
Figure 163532DEST_PATH_IMAGE032
。
In the method, in the third step, for the sample
Figure 774642DEST_PATH_IMAGE033
Defining new variables
Figure 675733DEST_PATH_IMAGE034
,
Figure 5084DEST_PATH_IMAGE035
,
Wherein
Figure 751454DEST_PATH_IMAGE036
，
Figure 482650DEST_PATH_IMAGE037
Is to be
Figure 85800DEST_PATH_IMAGE038
Obtained by performing transformation with respect to
Figure 636867DEST_PATH_IMAGE039
，
Figure 452508DEST_PATH_IMAGE040
The method only contains the information of the fault category but not the information of the working condition category, in order to distinguish the fault category, the cross entropy is adopted as an optimization function, and the expression is as follows:
Figure 38210DEST_PATH_IMAGE041
,
the entropy regularization of a single sample is defined as:
Figure 15524DEST_PATH_IMAGE042
wherein
Figure 319467DEST_PATH_IMAGE043
Is the number of the categories that the user is in,
Figure 673219DEST_PATH_IMAGE044
is that the sample is
Figure 113427DEST_PATH_IMAGE045
And (3) class probability, constructing an entropy regular loss function as follows:
Figure 792801DEST_PATH_IMAGE046
to achieve the desired output of the discriminator, the penalty function is:
Figure 69193DEST_PATH_IMAGE047
。
in the method, in the third step, the cross-entropy-like loss function is:
Figure 475904DEST_PATH_IMAGE048
,
the cross entropy of the domains is
Figure 786931DEST_PATH_IMAGE049
，
Wherein
Figure 620894DEST_PATH_IMAGE050
Is a conditional probability (
Figure 650161DEST_PATH_IMAGE051
) The loss function expression is as follows:
Figure 594984DEST_PATH_IMAGE052
。
in the method, in the first step, the bearing is a rolling bearing.
The invention has the beneficial effects that:
1) the deep learning is utilized, and the characteristics of strong data pattern deep mining capability and remarkable knowledge learning capability are achieved. Features can be automatically extracted from the original signal without using complex signal processing methods to rely on manual extraction of fault features. Therefore, compared with the traditional fault diagnosis method, the method is more efficient and has wider application range.
2) The problems that resources are wasted due to the fact that traditional artificial intelligence needs to use new working condition label-free data to participate in training in the variable working condition problem, and the new working condition data cannot be obtained in the training process in some cases are solved. The invention only uses a plurality of source domain working conditions, learns the invariant characteristics of the domain by a conditional countermeasure method and promotes the model to be in
The above description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly apparent, and to make the implementation of the content of the description possible for those skilled in the art, and to make the above and other objects, features and advantages of the present invention more obvious, the following description is given by way of example of the specific embodiments of the present invention.
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Various other advantages and benefits of the present invention will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. Also, like parts are designated by like reference numerals throughout the drawings.
In the drawings:
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a schematic diagram of the training phase of the present invention;
FIG. 3 is a schematic diagram of the output of the feature extractor expectation discriminator of the present invention;
FIG. 4 is a diagram of the network architecture of the present invention;
fig. 5(a) to 5(d) are feature cluster maps of two working condition samples of a source domain according to the case-source domain of the present invention, wherein fig. 5(a) is an original cluster map, fig. 5(b) is a cluster map of a conditional penalty function based on mean square error (mse) and (c) is a cluster map of a conditional penalty function based on cross entropy and entropy of classes, and fig. 5(d) is a cluster map of a conditional penalty function based on cross entropy and cross entropy of domains;
fig. 6(a) to 6(d) are feature cluster maps of two working condition samples of the case two-source domain of the present invention, wherein fig. 6(a) is an original cluster map, fig. 6(b) is a cluster map based on a mean square error conditional countermeasures loss function, fig. 6(c) is a cluster map based on cross entropy and entropy of a class, and fig. 6(d) is a cluster map based on cross entropy and cross entropy of a domain;
7(a) to 7(d) are feature cluster maps of two working condition samples of a case three-source domain of the invention, FIG. 7(a) an original cluster map, FIG. 7(b) a cluster map based on a mean square error conditional adversity loss function, FIG. 7(c) a cluster map of a conditional adversity loss function based on cross entropy and entropy of classes, and FIG. 7(d) a cluster map of a conditional adversity loss function based on cross entropy and cross entropy of domains;
8(a) to 8(d) are feature cluster maps of two working condition samples of a case four-source domain of the invention, FIG. 8(a) an original cluster map, FIG. 8(b) a cluster map based on a mean square error conditional adversity loss function, FIG. 8(c) a cluster map of a conditional adversity loss function based on cross entropy and entropy of classes, and FIG. 8(d) a cluster map of a conditional adversity loss function based on cross entropy and cross entropy of domains;
FIGS. 9(a) to 9(c) are graphs of the value of the objective domain loss function of case one of the present invention as a function of the number of iteration steps-
Figure 760517DEST_PATH_IMAGE053
The line of-' is the conventional CNN-
Figure 499803DEST_PATH_IMAGE054
-' is the conditional countermeasure adopted in the present invention; i is a method 1 based on a mean square error condition to confront a loss function; II, constructing a conditional adversity loss function based on the cross entropy and entropy of the class; III is constructing a conditional adversity loss function based on the cross entropy of the class and the cross entropy of the domain, FIG. 9(a) is a cluster map of the conditional adversity loss function based on the mean square error, FIG. 9(b) is a cluster map of the conditional adversity loss function based on the cross entropy and the entropy of the class, and FIG. 9(c) is a cluster map of the conditional adversity loss function based on the cross entropy and the entropy of the class;
fig. 10(a) to 10(c) are graphs of the target domain loss function values according to the iteration step number in case two of the present invention, where the line of "-" is the conventional CNN and "-" is the conditional countermeasure adopted by the present invention; i is a method 1 based on a mean square error condition to confront a loss function; II, constructing a conditional adversity loss function based on the cross entropy and entropy of the class; III is constructing a conditional adversity loss function based on the cross entropy of the class and the cross entropy of the domain, FIG. 10(a) is a cluster map of the conditional adversity loss function based on the mean square error, FIG. 10(b) is a cluster map of the conditional adversity loss function based on the cross entropy and entropy of the class, and FIG. 10(c) is a cluster map of the conditional adversity loss function based on the cross entropy and entropy of the class;
fig. 11(a) to 11(c) are graphs of the target domain loss function values according to the iteration step number for case three of the present invention, where the line of "-" is the conventional CNN and "-" is the conditional countermeasure adopted by the present invention; i is a method 1 based on a mean square error condition to confront a loss function; II, constructing a conditional adversity loss function based on the cross entropy and entropy of the class; III is constructing a conditional adversary loss function based on the cross entropy of the class and the cross entropy of the domain, FIG. 11(a) is a cluster map of the conditional adversary loss function based on the mean square error, FIG. 11(b) is a cluster map of the conditional adversary loss function based on the cross entropy and entropy of the class, and FIG. 11(c) is a cluster map of the conditional adversary loss function based on the cross entropy and entropy of the class;
fig. 12(a) to 12(c) are graphs of the target domain loss function value according to the iteration step number for case four of the present invention, the line of "-" is the conventional CNN, and "-" is the conditional countermeasure adopted by the present invention; i is a method 1 based on a mean square error condition to confront a loss function; II, constructing a conditional adversity loss function based on the cross entropy and entropy of the class; III is constructing a conditional adversary loss function based on the cross entropy of the class and the cross entropy of the domain, fig. 12(a) is a cluster map of the conditional adversary loss function based on the mean square error, fig. 12(b) is a cluster map of the conditional adversary loss function based on the cross entropy and entropy of the class, and fig. 12(c) is a cluster map of the conditional adversary loss function based on the cross entropy and entropy of the class.
The invention is further explained below with reference to the figures and examples.
Detailed Description
Specific embodiments of the present invention will be described in more detail below with reference to fig. 1 to 12 (c). While specific embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It should be noted that certain terms are used throughout the description and claims to refer to particular components. As one skilled in the art will appreciate, various names may be used to refer to a component. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. The description which follows is a preferred embodiment of the invention, but is made for the purpose of illustrating the general principles of the invention and not for the purpose of limiting the scope of the invention. The scope of the present invention is defined by the appended claims.
For the purpose of facilitating understanding of the embodiments of the present invention, the following description will be made by taking specific embodiments as examples with reference to the accompanying drawings, and the drawings are not to be construed as limiting the embodiments of the present invention.
The cross-working condition countermeasure diagnostic method of the bearing comprises the following steps:
in the first step, vibration data of the rolling bearing under a plurality of different working conditions are collected, and the working conditions are divided into source domain working conditions and target domain working conditions. Dividing the vibration data of the source domain working condition to generate signal samples, and taking the samples out of the source domain samples according to a certain proportion to serve as a training set for training; and the target domain vibration data is divided to generate signal samples which are used as test set samples of the test.
In the second step, a training module is constructed, wherein the training module comprises a feature extractor for extracting signal features, a classifier for classifying bearing faults and a discriminator for distinguishing the working conditions and faults of the features, the feature extractor is provided with feature extraction parameters, the classifier is provided with classifier parameters, and the discriminator is provided with discriminator parameters;
in the third step, the training module trains based on a training set, wherein the feature extraction parameters and the classifier parameters are updated according to a loss function by using a BP method (back propagation algorithm); fixing the characteristic extractor parameters, and updating discriminator parameters by using the loss function; fixing discriminator parameters, and updating the characteristic extractor parameters by using a countermeasure loss function; the step ensures that the influence of the working condition on the classification characteristic is considered as little as possible when the model extracts the characteristic through the invariant characteristic of the learning field,
and in the fourth step, a test module is constructed based on the updated classifier and the updated feature extractor, and a test set and/or a target domain working condition sample is input into the test module for fault diagnosis. This may test the network for fault diagnosis capability under unknown conditions.
In a preferred embodiment of the method, in a first step, the different operating conditions are divided into a source domain operating condition and a target domain operating condition. And (3) dividing the vibration data in the source domain working condition to generate signal samples with the length of 1024, and taking out the source domain working condition samples according to a certain proportion to be used as a training set for training. And dividing the vibration data of the target domain working condition into 1024-length signal samples, and dividing the signal samples into a test set.
In a preferred embodiment of the method, in a first step, the vibration data is divided into 1024-length signal samples, and the test set and the training set for testing are randomly divided according to a predetermined ratio, wherein each signal sample from the test set and the training set is normalized,
Figure 16366DEST_PATH_IMAGE055
，
Figure 764879DEST_PATH_IMAGE056
are samples of the signal generated from the raw vibration data,
Figure 784919DEST_PATH_IMAGE057
is that
Figure 960685DEST_PATH_IMAGE058
The average value of (a) of (b),
Figure 698965DEST_PATH_IMAGE059
is that
Figure 985590DEST_PATH_IMAGE056
Standard deviation of (2).
In a preferred embodiment of the method, in the second step, the training set is a source domain data set of known fault labels and operating condition information
Figure 379576DEST_PATH_IMAGE005
，
Figure 726244DEST_PATH_IMAGE060
Wherein the source domain data set
Figure 686240DEST_PATH_IMAGE005
Is provided with
Figure 776556DEST_PATH_IMAGE007
A sample,
Figure 771188DEST_PATH_IMAGE008
Class and
Figure 288757DEST_PATH_IMAGE009
the working conditions of the individual source areas are set,
Figure 267209DEST_PATH_IMAGE061
the working condition serial number of the source domain;
Figure 646369DEST_PATH_IMAGE017
is the ith sample, which is from the ith
Figure 744775DEST_PATH_IMAGE010
The working condition of the source region belongs to
Figure 183977DEST_PATH_IMAGE012
Class, which is labeled as
Figure 633413DEST_PATH_IMAGE062
To a
Figure 550685DEST_PATH_IMAGE063
Discriminator label
Figure 769176DEST_PATH_IMAGE064
The expression is as follows:
Figure 644860DEST_PATH_IMAGE016
to a
Figure 316012DEST_PATH_IMAGE065
Classifier (A)
Figure 36975DEST_PATH_IMAGE018
) The output is:
Figure 109973DEST_PATH_IMAGE066
wherein
Figure 625399DEST_PATH_IMAGE067
,
Figure 49427DEST_PATH_IMAGE068
Is that the sample belongs to
Figure 574081DEST_PATH_IMAGE022
Class, discriminator output is:
Figure 767165DEST_PATH_IMAGE069
,
wherein
Figure 984650DEST_PATH_IMAGE070
Wherein, in the step (A),
Figure 99237DEST_PATH_IMAGE025
representative sample
Figure 427581DEST_PATH_IMAGE014
Belong to the first
Figure 475172DEST_PATH_IMAGE010
Second of the individual domain
Figure 597980DEST_PATH_IMAGE012
Probability of individual class, the ideal output of the discriminator required by the feature extractor is:
Figure 465442DEST_PATH_IMAGE071
and
Figure 331897DEST_PATH_IMAGE072
in a similar manner to the above-described embodiments,
Figure 499574DEST_PATH_IMAGE073
representing samples at ideal output of discriminator
Figure 793283DEST_PATH_IMAGE014
Belong to the first
Figure 413620DEST_PATH_IMAGE010
Second of the individual domain
Figure 829906DEST_PATH_IMAGE012
Probability of individual class.
In a preferred embodiment of the method, in the third step, the first step uses cross entropy as a loss function, and updates the feature extraction parameter and the classifier parameter according to the loss function by using a BP method, wherein the cross entropy expression of the small step loss function is
Figure 852089DEST_PATH_IMAGE074
。
In a preferred embodiment of the method, in the third step, the second substep fixes the feature extractor parameters and updates only the discriminator parameters by using a cross entropy loss function, the expression of which is
Figure 316700DEST_PATH_IMAGE075
。
In a preferred embodiment of the method, the 3 rd step in the third step is a total of three loss functions.
In a preferred embodiment of said method, in the third step, the penalty-combating function is based on the mean square error, expressed as
Figure 158754DEST_PATH_IMAGE076
。
In a preferred embodiment of said method, in a third step, the cross entropy is used as a loss function for the samples
Figure 367012DEST_PATH_IMAGE077
Defining new variables
Figure 509281DEST_PATH_IMAGE040
,
Figure 144792DEST_PATH_IMAGE078
,
Wherein
Figure 474143DEST_PATH_IMAGE079
，
Figure 486092DEST_PATH_IMAGE037
Is to be
Figure 217288DEST_PATH_IMAGE038
Obtained by performing transformation with respect to
Figure 23701DEST_PATH_IMAGE039
，
Figure 856659DEST_PATH_IMAGE040
The method only contains information of categories but not information of working conditions, in order to realize category discrimination, cross entropy is adopted as an optimization function, and the expression is as follows:
Figure 655988DEST_PATH_IMAGE080
,
the entropy of a single sample is regular as:
Figure 241690DEST_PATH_IMAGE081
wherein
Figure 219004DEST_PATH_IMAGE043
Is the number of the categories that the user is in,
Figure 522946DEST_PATH_IMAGE044
is that the sample is
Figure 876698DEST_PATH_IMAGE045
Probability of class, entropy loss function is:
Figure 316907DEST_PATH_IMAGE082
，
the penalty function is:
Figure 996281DEST_PATH_IMAGE083
。
in a preferred embodiment of said method, in the third step, the cross-entropy loss function is
Figure 272673DEST_PATH_IMAGE084
，
Figure 679384DEST_PATH_IMAGE085
Wherein
Figure 990410DEST_PATH_IMAGE086
Conditional probability (
Figure 824374DEST_PATH_IMAGE087
) The penalty function expression is:
Figure 588062DEST_PATH_IMAGE088
。
in a preferred embodiment of the method, in the first step, the bearing is a rolling bearing.
In order to further understand the invention, the invention develops a bearing diagnosis model training method, and a traction motor bearing fault diagnosis model can be constructed by using the method. After the model is trained and trained based on a plurality of source domain working condition data, the generalization capability of the model in a rotating speed and load combined test set which does not appear in a training set can be improved.
The invention is generally divided into the following three modules of data acquisition and processing, a training module and a testing module:
1) the data acquisition and processing module mainly refers to the acquisition of data, the division of a data set and the normalization of a sample
2) The training module comprises three parts (1) a characteristic extraction module
Figure 532884DEST_PATH_IMAGE089
With the parameter of
Figure 698417DEST_PATH_IMAGE090
): extracting deep-layer characteristics of signals by utilizing a plurality of working condition time-domain vibration signals acquired by a bearing fault prefabrication experiment; 2. classifier module (
Figure 968862DEST_PATH_IMAGE018
With the parameter of
Figure 219846DEST_PATH_IMAGE091
): using 1) general-purpose based on extracted deep features
Figure 233938DEST_PATH_IMAGE092
And (4) completing fault classification of the motor bearing. 3. Discriminator module (
Figure 253978DEST_PATH_IMAGE093
With the parameter of
Figure 429744DEST_PATH_IMAGE094
): based on the extracted features in 1), the discriminator must distinguish which condition the generated features come from and what kind of fault.
3) The test module comprises two parts, namely 2) a feature extraction module
Figure 421885DEST_PATH_IMAGE089
) And a classifier module (
Figure 459242DEST_PATH_IMAGE018
). During testing, the load and rotating speed combined condition data which do not appear in training are collected and used for testing
The first step of the present invention is the acquisition and processing of data. And (3) mounting a sensor at a proper position, and acquiring vibration data of the bearing in an operating state. The collected data is divided into 1024-length samples, and the source domain samples are taken out according to the proportion of 80% to be used as a training set for training. Each sample from the test set and training set is normalized by mean-std.
Figure 114214DEST_PATH_IMAGE095
（1）
The second and third embodiments of the present invention will be described in more detail with reference to the accompanying drawings.
In the present invention, as shown in fig. 1, the overall structural diagram of the method is generally described. The method is described in detail in the following order.
The training phase is divided into three steps in total as shown in fig. 2.
The first step is to use the cross entropy as a loss function and update the characteristic extraction parameters and classifier parameters of the model according to the cross entropy loss function by using a BP method.
And secondly, fixing the parameters of the feature extractor, and updating the parameters of the discriminator by using a cross entropy loss function, so that the discriminator can distinguish which working condition and which fault type the sample comes from as far as possible.
Thirdly, the parameters of the discriminator are fixed and the parameters of the feature extractor are updated by utilizing the combination of the three loss functions of the invention, thereby achieving the effect of confusing the discriminator. By using the loss function combination of the invention, the parameters of the discriminator are fixed, and the parameters of the feature extractor are updated, thus achieving the effect of confusing the discriminator. One core of the invention is that the obfuscator is not a traditional full obfuscation, but a conditional obfuscation, i.e. a conditional countermeasure. The present invention requires that the feature extractor can make the discriminator unable to distinguish which condition the sample is from but can distinguish which type of sample the sample is from. By using the conditional countermeasure method, the interference of the working conditions on the feature extractor can be reduced, so that the feature extractor has the capability of extracting the domain-independent features. By utilizing the capability, the network test set and the diagnosis accuracy rate under unknown working conditions can be improved.
And in the testing stage, only the feature extractor and the classifier obtained by the countertraining are used, and the target domain working condition sample which does not appear in the training is input for fault diagnosis.
In one aspect of the invention, the construction of the conditional penalty function:
variables and problem descriptions are defined first to facilitate the following description.
Figure 946035DEST_PATH_IMAGE060
Representing source domain data sets, in common
Figure 686458DEST_PATH_IMAGE007
A sample, in total
Figure 261927DEST_PATH_IMAGE008
Class to
Figure 505826DEST_PATH_IMAGE010
All working conditions are common
Figure 39707DEST_PATH_IMAGE096
（
Figure 470688DEST_PATH_IMAGE097
) The characteristics of each sample, source domain, are the label and working condition information of the known fault
Figure 584269DEST_PATH_IMAGE005
Therein is provided with
Figure 948254DEST_PATH_IMAGE009
And (4) operating conditions of each source domain.
Figure 387457DEST_PATH_IMAGE098
Represents the target domain dataset, but does not know the category information and does not participate in the training.
Figure 305734DEST_PATH_IMAGE099
Is from the first
Figure 472274DEST_PATH_IMAGE010
A working condition belongs to
Figure 441498DEST_PATH_IMAGE012
Class one to
Figure 35290DEST_PATH_IMAGE100
A specimen labeled as
Figure 457175DEST_PATH_IMAGE101
For discriminators
Figure 161826DEST_PATH_IMAGE102
The expression is as follows:
Figure 985557DEST_PATH_IMAGE016
（2）
for the
Figure 750250DEST_PATH_IMAGE065
The classifier passes through
Figure 643120DEST_PATH_IMAGE092
The output is:
Figure 167773DEST_PATH_IMAGE103
（3）
wherein
Figure 95278DEST_PATH_IMAGE104
.
Discriminator pass through
Figure 312764DEST_PATH_IMAGE092
The output is:
Figure 958509DEST_PATH_IMAGE105
（4）
wherein
Figure 286853DEST_PATH_IMAGE106
Each element in the formula represents a type of fault under one operating condition, i.e.
Figure 68865DEST_PATH_IMAGE025
Represents the first
Figure 191672DEST_PATH_IMAGE010
Second of the individual domain
Figure 324714DEST_PATH_IMAGE012
A class of the one or more classes,
Figure 191170DEST_PATH_IMAGE107
the calculation method of (A) is shown as the formula. The core idea of the invention is to let the discriminator distinguish which class a sample comes from but not which condition it comes from. The task for a discriminator is very simple, namely to distinguish which class and which domain the sample comes from, and the target output of the discriminator is shown by the equation:
Figure 827687DEST_PATH_IMAGE108
（5）
for the feature extractor, the goal is to make the discriminator distinguish which class the sample comes from but not which condition it comes from, as shown in fig. 3, the target output expression is:
Figure 121396DEST_PATH_IMAGE109
（6）
fig. 3 illustrates a sample of the first category for the first operating mode to explain how the core objectives of the present invention are achieved. In fig. 3, the circle represents a first operating condition, and the square represents a second operating condition. The goal of the generator requires that the sum of the first element of the circle box and the first element of the box (the first element representing a first type of failure) be 1. By summing to 1 it is ensured that the discriminator can distinguish which class the sample belongs to. However, in order for the discriminator to be unable to distinguish which case it belongs to, it is required that the first element of the circle and the first element of the square be both 0.5 (i.e., that is, that of the square)
Figure 741734DEST_PATH_IMAGE110
For the present example
Figure 892441DEST_PATH_IMAGE111
). To implement the above-mentioned patent core idea, the present invention is shown in fig. 2 at each iteration of the training phase.
The method comprises the following specific steps:
step 1 training feature extractor and classifier
Similar to the traditional deep learning network, the invention uses a cross-entropy loss function in the first step (
Figure 649044DEST_PATH_IMAGE112
As shown) as an optimization objective to update parameters
Figure 113655DEST_PATH_IMAGE090
，
Figure 955709DEST_PATH_IMAGE091
At the time of optimizationThe cross entropy loss function is reduced as much as possible by using the BP algorithm.
Figure 163967DEST_PATH_IMAGE113
（7）
Step 2 training discriminator
As shown in the equation, the goal of the discriminator is to distinguish which condition and which class the sample comes from, and its nature remains a classification problem. Therefore, similar to step 1, the invention selects the cross entropy as the loss function (as shown in the formula), and fixes the cross entropy
Figure 775077DEST_PATH_IMAGE090
Update
Figure 659857DEST_PATH_IMAGE094
。
Figure 739939DEST_PATH_IMAGE114
（8）
Step 3 training feature extractor
This step is the core of the present invention, and the main purpose of this step is to realize that the discriminator can distinguish which class the sample comes from but cannot distinguish which condition it comes from, and the core goal is formula. To achieve this goal, the present invention solves this problem with three loss functions.
The method comprises the following steps: it is straightforward to let the discriminator output resemble its target as much as possible, treat this problem as a fitting problem, and let the discriminator output fit its target output, the most common method in fitting being mean error square (MSE). Thus, the first conditional penalty of step 3 is (based on the mean square error conditional penalty function):
Figure 735577DEST_PATH_IMAGE115
（9）
the method 2 comprises the following steps: the core idea of the invention is that the discriminator can distinguish between classes, but cannotAnd distinguishing the working conditions. The idea can be broken down into two parts, the first part distinguishes the category, and the second part cannot distinguish the working condition. To achieve class discrimination, cross entropy can be used as a loss function, for ease of description, for samples
Figure 217505DEST_PATH_IMAGE033
Defining new variables
Figure 7607DEST_PATH_IMAGE116
。
Figure 309406DEST_PATH_IMAGE117
（10）
Wherein
Figure 108735DEST_PATH_IMAGE118
，
Figure 445169DEST_PATH_IMAGE040
Is to be
Figure 671751DEST_PATH_IMAGE039
Obtained by performing transformation with respect to
Figure 975694DEST_PATH_IMAGE119
，
Figure 63867DEST_PATH_IMAGE120
Only the category information is included and the condition information is not included. In order to realize category discrimination, cross entropy is adopted as an optimization function, and the expression is as follows:
Figure 504075DEST_PATH_IMAGE121
（11）
in order to realize the condition that the working condition cannot be distinguished, the entropy regulation is adopted to solve the problem, and for a single sample, the entropy regulation is as follows:
Figure 652291DEST_PATH_IMAGE122
（12）
wherein
Figure 443529DEST_PATH_IMAGE043
Is the number of categories, is
Figure 335393DEST_PATH_IMAGE044
The sample is the first
Figure 895688DEST_PATH_IMAGE045
The probability of a class. When entcopy is minimum, the sample class is determined. When the amount of the drug is the maximum,
Figure 214805DEST_PATH_IMAGE123
i.e. from which class the sample cannot be distinguished. Based on the above expression, entropy regularization is employed to solve this problem. Thus the second method is to construct a loss function (a conditional countermeasures loss function based on cross-entropy and entropy of classes) as
Figure 227760DEST_PATH_IMAGE124
（13）
Thus the second method is to construct a loss function (a conditional countermeasures loss function based on cross-entropy and entropy of classes) as
Figure 657735DEST_PATH_IMAGE125
（14）
Method 3 if the class is known, a simple way to distinguish the operating conditions is to use cross entropy, and to achieve this purpose, the operating conditions can be distinguished using equations.
Figure 72536DEST_PATH_IMAGE126
（15）
Wherein
Figure 93713DEST_PATH_IMAGE127
Conditional probability (
Figure 593964DEST_PATH_IMAGE128
) Distinguishing from which condition the sample came from can be achieved by minimizing the equation. However, the aim of the invention is to achieve a discriminator that does not distinguish from which condition the sample came from, and therefore maximizes the optimization
Figure 562052DEST_PATH_IMAGE129
. Therefore, the expression of the loss function (cross entropy + conventional impedance function: cross-entropy + conventional adaptive method) of method 3 is:
Figure 96938DEST_PATH_IMAGE130
（16）
according to the above explanation, the steps of the present invention are as follows:
1 minimization, updating parameters
Figure 23437DEST_PATH_IMAGE090
，
Figure 745405DEST_PATH_IMAGE091
To make
Figure 32030DEST_PATH_IMAGE018
，
Figure 640997DEST_PATH_IMAGE089
The fault may be classified.
2 fixed parameters
Figure 722086DEST_PATH_IMAGE090
Minimization, updating parameters
Figure 935943DEST_PATH_IMAGE094
To make
Figure 26259DEST_PATH_IMAGE093
Can distinguish which working condition and which type of fault sample comes from
3 fixed parameters
Figure 755311DEST_PATH_IMAGE094
Selecting any one from the formulas as an optimization parameter, and updating the parameter
Figure 7301DEST_PATH_IMAGE090
Through conditional countermeasure training
Figure 703862DEST_PATH_IMAGE089
Domain invariant models can be learned, i.e. lets
Figure 83022DEST_PATH_IMAGE093
The categories can be distinguished, but the conditions cannot be distinguished.
4 utilization of
Figure 181428DEST_PATH_IMAGE018
，
Figure 355051DEST_PATH_IMAGE089
And performing fault diagnosis, and inputting samples of the source domain working condition and the target domain working condition which does not appear in training for fault diagnosis.
In one embodiment of the present invention, the substrate is,
through a traction motor bearing acceleration prefabrication fault test, taking non-driving-end 6311 bearing data as an example, a three-way acceleration sensor is used for measuring time domain vibration data of three directions of the traction motor bearing from X, Y, Z
Figure 538908DEST_PATH_IMAGE131
And collecting the collected data. For convenient operation, selecting
Figure 721759DEST_PATH_IMAGE132
The acceleration vibration signal time domain of the shaft is taken as a sample. In the example there are 6 faults in total, as shown in table 1. The source domain operating conditions and the target domain operating conditions are shown in table 2.
TABLE 1 failure types
Figure 409092DEST_PATH_IMAGE133
TABLE 2 working condition table
Figure 19196DEST_PATH_IMAGE134
Each sample length is 1024, normalized by mean-std (formula):
Figure 690349DEST_PATH_IMAGE135
wherein
Figure 660579DEST_PATH_IMAGE058
Is the original signal of the input samples and,
Figure 218730DEST_PATH_IMAGE136
is that
Figure 514582DEST_PATH_IMAGE058
The average value of (a) of (b),
Figure 767971DEST_PATH_IMAGE137
is that
Figure 541892DEST_PATH_IMAGE058
Standard deviation of (2).
The overall architecture of the network is shown in fig. 4, where the feature extractor framework refers to the structure of resnet18 with the addition of bottleeck, whose parameters are shown in table 3. The classifier and discriminator parameters are shown in tables 4 and 5. The hyper-parameters and the computer configuration are shown in tables 6 and 7.
TABLE 3 feature extractor
(a) Resnet architecture
Figure 485709DEST_PATH_IMAGE138
(b) Bottleneck architecture
Figure 952462DEST_PATH_IMAGE139
TABLE 4 classifier (C) architecture
Figure 348939DEST_PATH_IMAGE140
TABLE 5 discriminator network architecture
Figure 660972DEST_PATH_IMAGE141
In one aspect of the invention, the construction of the penalty function is conditioned during the training phase.
The computer configurations and hyper-parameters used in the training of the present invention are shown in tables 6 and 7.
TABLE 6 computer hardware and configuration
Figure 724874DEST_PATH_IMAGE142
TABLE 7 Superparameter
Figure 847682DEST_PATH_IMAGE143
In the training phase, each iteration step performs the following three steps.
The method comprises the steps of preprocessing data, dividing a vibration signal of a source domain working condition 1, a source domain working condition 2 and a target domain working condition into samples with the length of 1024, and marking the source domain working condition with a classifier label and a discriminator label. Target domain condition labeled classifier label
1, inputting the samples of the working condition 1 and the working condition 2 of the source domain into a characteristic extractor, and obtaining the prediction result of the samples by a classifier
The classifier obtains the prediction result of the sample
Figure 511882DEST_PATH_IMAGE144
. And optimizing the characteristic extractor and the classifier parameters by using a BP algorithm and adopting an adam optimizer to minimize cross entropy (formula).
Figure 909496DEST_PATH_IMAGE145
（18）
2, inputting the samples of the working condition 1 and the working condition 2 of the source domain into a characteristic extractor, and obtaining which category the samples come from by a discriminator. Using the BP algorithm, only discriminator parameters are optimized using the adam optimizer to minimize cross entropy (formula). (this step is performed twice per iteration)
Figure 827905DEST_PATH_IMAGE146
（19）
And 3, inputting the source domain working condition 1 and working condition 2 samples into a characteristic extractor together, and obtaining which class the samples come from by a discriminator. By utilizing a BP algorithm, an adam optimizer optimizes parameters of the feature extractor by adopting one of a conditional counterattack loss function (formula) based on mean square error, a conditional counterattack loss function (formula) based on cross entropy and entropy of a class and a conditional counterattack loss function (formula) based on cross entropy and cross entropy of a domain, so that the discriminator can distinguish the class of a sample, but cannot distinguish which working condition the fault comes from. In the third step of training, the invention provides three loss functions, namely a countermeasure loss function based on a mean square error condition:
Figure 636461DEST_PATH_IMAGE147
（20）
construction of conditional adversarial loss function based on class cross entropy and entropy
Figure 741951DEST_PATH_IMAGE148
（21）
Conditional penalty function based on mean square error
Figure 661366DEST_PATH_IMAGE149
（22）
And 4, after one iteration is finished, inputting the target domain working condition test sample into the feature extractor and the classifier, and testing the performance of the network. See fig. 9-12.
TABLE 8 accuracy Table for four cases
(a) Case 1
Figure 727341DEST_PATH_IMAGE150
(b) Case 2
Figure 175640DEST_PATH_IMAGE151
(c) Case 3
Figure 34005DEST_PATH_IMAGE152
(d) Case 4
Figure 225952DEST_PATH_IMAGE153
Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments and application fields, and the above-described embodiments are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.

Claims (6)

1. A cross-condition countermeasure diagnostic method for a bearing, the method comprising the steps of:
the method comprises the steps that in the first step, vibration data of a rolling bearing under a plurality of different working conditions are collected, wherein the working conditions are divided into a source domain working condition and a target domain working condition; dividing the vibration data under the working condition of the source domain to generate a first signal sample, and taking out the signal sample according to 80% of proportion to be used as a training set for training; and dividing the vibration data under the working condition of the target domain to generate a second signal sample which is then used as a test set sample for testing, dividing the vibration data to generate signal samples with the length of 1024 data points, and randomly dividing the test set for testing and a training set for training according to a preset proportion, wherein each signal sample from the test set and the training set is subjected to normalization processing, and the expression is as follows:
Figure 537015DEST_PATH_IMAGE001
，
Figure 693715DEST_PATH_IMAGE002
are samples of the signal generated by the vibration data,
Figure 606307DEST_PATH_IMAGE003
is that
Figure 478317DEST_PATH_IMAGE002
The average value of (a) of (b),
Figure 282194DEST_PATH_IMAGE004
is that
Figure 41202DEST_PATH_IMAGE002
The standard deviation of (a) is determined,
Figure 108384DEST_PATH_IMAGE005
representing the signal obtained after the normalization process;
in the second step, a training module is constructed, which comprises a feature extractor for extracting signal features, and a classification for classifying bearing faultsThe system comprises a characteristic extractor and a discriminator for distinguishing the working condition and the fault of the characteristic, wherein the characteristic extractor is provided with characteristic extractor parameters, the classifier is provided with classifier parameters, the discriminator is provided with discriminator parameters, and the training set is a source domain data set of known fault labels and working condition information
Figure 470620DEST_PATH_IMAGE006
The expression is as follows:
Figure 78188DEST_PATH_IMAGE007
，
wherein the content of the first and second substances,
source domain data set
Figure 957282DEST_PATH_IMAGE006
Is provided with
Figure 992103DEST_PATH_IMAGE008
A sample,
Figure 589438DEST_PATH_IMAGE009
Class and
Figure 696DEST_PATH_IMAGE010
source domain conditions;
Figure 265455DEST_PATH_IMAGE011
the working condition serial number of the source domain;
Figure 739687DEST_PATH_IMAGE012
is the ith sample, which is from the ith
Figure 824317DEST_PATH_IMAGE013
Individual source region working condition and belongs to
Figure 773688DEST_PATH_IMAGE014
Class, which is labeled as
Figure 142221DEST_PATH_IMAGE015
；
For the
Figure 535156DEST_PATH_IMAGE012
Discriminator tag of
Figure 621930DEST_PATH_IMAGE016
The expression is as follows:
Figure 125723DEST_PATH_IMAGE017
；
for the
Figure 410332DEST_PATH_IMAGE012
Output of the classifier (C)
Figure 974169DEST_PATH_IMAGE018
The expression of (a) is:
Figure 17080DEST_PATH_IMAGE019
，
wherein the content of the first and second substances,
Figure 324565DEST_PATH_IMAGE020
，
Figure 667690DEST_PATH_IMAGE021
is a sample
Figure 136849DEST_PATH_IMAGE022
Belong to the first
Figure 667056DEST_PATH_IMAGE023
The probability of a class;
actual output of discriminator
Figure 499270DEST_PATH_IMAGE024
The expression of (a) is:
Figure 182056DEST_PATH_IMAGE025
，
wherein the content of the first and second substances,
Figure 71383DEST_PATH_IMAGE026
；
Figure 105198DEST_PATH_IMAGE027
representing the actual output of the discriminator, the sample
Figure 3753DEST_PATH_IMAGE028
Probability of belonging to class j;
the feature extractor requires the ideal output of the discriminator
Figure 55891DEST_PATH_IMAGE029
The expression of (a) is:
Figure 132432DEST_PATH_IMAGE030
，
wherein the content of the first and second substances,
Figure 640161DEST_PATH_IMAGE031
sample when the representative feature extractor requires the ideal output of the discriminator
Figure 827560DEST_PATH_IMAGE032
Probability of belonging to class j;
in a third step, training the training module based on a training set, comprising:
updating the feature extractor parameters and the classifier parameters according to a first loss function by using a BP method;
fixing the characteristic extractor parameters, and updating discriminator parameters by using a second loss function;
fixing discriminator parameters, and updating the characteristic extractor parameters by using a countermeasure loss function;
and in the fourth step, a test module is constructed based on the updated classifier and the updated feature extractor, and a test set sample is input into the test module for fault diagnosis.
2. The method according to claim 1, wherein in the third step, the feature extractor parameters and the classifier parameters are updated by a BP method according to a first loss function using cross entropy as a loss function, and the first loss function
Figure 999784DEST_PATH_IMAGE033
For cross entropy, the expression is:
Figure 247226DEST_PATH_IMAGE034
；
wherein the content of the first and second substances,
Figure 239322DEST_PATH_IMAGE035
is a sample
Figure 964832DEST_PATH_IMAGE036
The probability of belonging to the k-th class,
Figure 725984DEST_PATH_IMAGE037
indicating the nth source domain condition-common
Figure 144327DEST_PATH_IMAGE037
And (4) sampling.
3. The method according to claim 1, wherein in a third step, the feature extractor parameters are fixed and the discriminator parameters are updated with a second loss function, the second loss functionNumber of
Figure 626648DEST_PATH_IMAGE038
The expression is as follows:
Figure 405117DEST_PATH_IMAGE039
，
wherein j is
Figure 37087DEST_PATH_IMAGE040
，NnIndicating the nth source domain condition-N in totalnAnd (4) sampling.
4. The method of claim 1, wherein in the third step, the antagonistic loss function is
Figure 610019DEST_PATH_IMAGE041
，
Figure 61860DEST_PATH_IMAGE042
Based on the mean square error, the expression is:
Figure 378441DEST_PATH_IMAGE043
，
wherein N isnIndicating the nth source domain condition-N in totalnAnd (4) sampling.
5. The method of claim 1, wherein in the third step, for the sample
Figure 370975DEST_PATH_IMAGE044
Defining new variables
Figure 865541DEST_PATH_IMAGE045
The expression is as follows:
Figure 53946DEST_PATH_IMAGE046
，
wherein;
Figure 190529DEST_PATH_IMAGE047
，
wherein the content of the first and second substances,
Figure 780780DEST_PATH_IMAGE048
is to be
Figure 695515DEST_PATH_IMAGE049
And (3) transforming to obtain: relative to
Figure 387527DEST_PATH_IMAGE050
，
Figure 314420DEST_PATH_IMAGE051
Only the information of the fault category is contained in the test data, but the information of the working condition category is not contained;
in order to distinguish fault categories, cross entropy is adopted to define an optimization function
Figure 775488DEST_PATH_IMAGE052
The expression is as follows:
Figure 861124DEST_PATH_IMAGE053
，
wherein N isnIndicating the nth source domain condition-N in totalnA sample is obtained;
the entropy of a single sample is regularly defined as the following expression:
Figure 40433DEST_PATH_IMAGE054
，
wherein the content of the first and second substances,
Figure 502507DEST_PATH_IMAGE055
is the number of the categories that the user is in,
Figure 818082DEST_PATH_IMAGE056
is that the sample is
Figure 340199DEST_PATH_IMAGE057
The probability of a class;
construction of entropy canonical loss function
Figure 462263DEST_PATH_IMAGE058
The expression is as follows:
Figure 478761DEST_PATH_IMAGE059
，
wherein j is
Figure 898110DEST_PATH_IMAGE060
；
To achieve the desired output of the discriminator, the penalty function is
Figure 591128DEST_PATH_IMAGE061
The expression is as follows:
Figure 479450DEST_PATH_IMAGE062
。
6. the method of claim 5, wherein,
defining a loss function using cross entropy of domains
Figure 283327DEST_PATH_IMAGE063
The expression is as follows:
Figure 307914DEST_PATH_IMAGE064
，
wherein the content of the first and second substances,
Figure 174764DEST_PATH_IMAGE065
is a conditional probability
Figure 550381DEST_PATH_IMAGE066
Domain represents a domain;
Figure 157949DEST_PATH_IMAGE067
representing the actual output of the discriminator, the sample
Figure 551890DEST_PATH_IMAGE068
Belong to the first
Figure 603023DEST_PATH_IMAGE069
The probability of a class;
Figure 449625DEST_PATH_IMAGE070
then represents the actual output of the discriminator, the sample
Figure 611616DEST_PATH_IMAGE068
Belong to the first
Figure 140291DEST_PATH_IMAGE071
The probability of a class;
the penalty function consists of
Figure 831167DEST_PATH_IMAGE072
Instead, the expression is:
Figure 430644DEST_PATH_IMAGE073
。
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