CN109947086B - Mechanical fault migration diagnosis method and system based on counterstudy - Google Patents
Mechanical fault migration diagnosis method and system based on counterstudy Download PDFInfo
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Abstract
The invention discloses a mechanical fault migration diagnosis method and system based on countermeasure learning, wherein the method comprises the following steps: acquiring original signals of mechanical faults under different working conditions, and analyzing to generate a source domain training data set with a label, a source domain training data set without a label and a target domain testing data set under different working conditions; training a deep convolutional neural network model according to a source domain training data set with a label and a back propagation algorithm to generate a fault diagnosis model; training a fault diagnosis model according to the source domain training data set without the label and the target domain testing data set; fine-tuning the trained fault diagnosis model according to the source domain training data set with the label and a back propagation algorithm; inputting the target domain test data set without the label into the fault diagnosis model after fine adjustment, and outputting the fault category of the sample to be tested. The method obtains the domain invariant feature through the counterstudy method, realizes the migration before different domains, and realizes the intelligent diagnosis of the variable working condition mechanical fault.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of mechanical equipment, in particular to a mechanical fault migration diagnosis method and system based on countermeasure learning.
Background
With the gradual development of industrial technology, the demand for industrial equipment is continuously increased, the integrated scale of the current industrial system is larger and larger, the structure of a single equipment is more and more complex, the coupling degree between different equipment in the same system is higher and higher, the factors lay a solid foundation for realizing complex behaviors of mechanical equipment on one hand, and meanwhile, the probability of the fault of the whole system is greatly increased.
The existing industrial system usually operates continuously and stably for a long time, the failure frequency is low, once the failure occurs, the failure deterioration speed is high, the failure causes great threat, and if the failure is not controlled in time, the serious accident of machine damage and human death can be caused. Therefore, the method has important social significance for ensuring the long-period stable operation of the industrial system and avoiding serious accidents, and can bring great economic and social benefits.
In order to guarantee the safety of an industrial system and promote the development of intelligent manufacturing, more and more industrial complex systems utilize an industrial Internet of things platform to establish an equipment operation state monitoring system, so that the industrial system collects and stores massive industrial equipment operation data, and a sufficient data source is provided for a data-driven fault diagnosis method. However, the opportunity is often accompanied by a challenge, and the industrial system has the characteristics of high environmental complexity, incomplete information and the like, so that the fault diagnosis technology based on data driving has a huge challenge in real application. For the fault diagnosis technology based on data driving, the main problems are derived from data, due to the characteristics of industrial equipment, the difficulty of collecting fault samples of the equipment is high, the existing actual fault data are insufficient in types and few in samples, the data are incomplete, and the migration learning method is a key technology for solving the problem.
The deep learning model based on data driving has strong dependence on samples, and a large amount of sample data is needed to train to obtain a model meeting actual needs. The good effect of the depth model requires that two conditions are met simultaneously: 1) there are sufficient training samples; 2) the distribution of the training data and the target application data should be as similar as possible. However, in an actual fault diagnosis task, the two conditions are often difficult to be simultaneously satisfied, for example, an industrial system usually needs to adjust power load according to actual requirements, and the change of the load changes spatial distribution of data obtained by an acquisition system, and when a model obtained by training data collected under a low-load condition is used for diagnosing data under a high-load condition, the performance of the model is greatly affected. However, collecting data again under target application test conditions can be costly and, in many cases, much less likely to be practical. Thus, migrating an already trained diagnostic model based on source domain training data and target application test data is an important means to solve this problem.
At present, the problem of migration fault diagnosis still belongs to the preliminary stage, and a great number of technical difficulties need to be deeply researched and broken through. The method for diagnosing the mechanical fault migration based on the counterstudy under the variable working conditions is an effective technical method for solving the technical problem.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, one objective of the present invention is to provide a mechanical fault migration diagnosis method based on countermeasure learning, which obtains a domain invariant feature through the countermeasure learning method, thereby implementing migration before different domains, and implementing intelligent diagnosis of variable-condition mechanical faults.
Another object of the present invention is to provide a mechanical fault migration diagnostic system based on countermeasure learning.
In order to achieve the above object, an embodiment of an aspect of the present invention provides a method for diagnosing mechanical fault migration based on countermeasure learning, including: s1, acquiring original signals of mechanical faults of a sample to be tested under different working conditions, and analyzing the original signals of the mechanical faults under different working conditions to generate a source domain training data set with a label, a source domain training data set without the label and a target domain testing data set without the label under different working conditions;
s2, training a deep convolutional neural network model according to the labeled source domain training data set and the back propagation algorithm, and generating a fault diagnosis model;
s3, performing migration training on the fault diagnosis model according to the unlabeled source domain training data set, the unlabeled target domain testing data set, a migration method of countermeasure learning and a Wasserstein distance-guided countermeasure network;
s4, fine-tuning the fault diagnosis model after migration training according to the source domain training data set with the label and a back propagation algorithm;
and S5, inputting the target domain test data set without the label into the fault diagnosis model after fine adjustment, and outputting the fault category of the sample to be tested.
According to the mechanical fault migration diagnosis method based on the countermeasure learning, the original signals of the mechanical faults under different working conditions are obtained, and source domain training data with labels and target domain test data without the labels are generated; optimizing a convolutional neural network serving as a basic diagnosis model by using a back propagation algorithm by using source domain training data with labels; obtaining a domain invariant feature by using source domain training data without a label and target domain test data through a Wasserstein distance-guided counterstudy method, and realizing cross-domain migration; the source domain training data with the labels are utilized, and a back propagation algorithm is used for fine tuning of the migration model, so that the problem of over-migration is avoided; and inputting the data of the label-free target domain to be tested into the migrated model to obtain a fault classification result.
In addition, the mechanical failure migration diagnosis method based on the countermeasure learning according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the S2 further includes:
s21, the labeled source domain training data set X is processedsRandomly distributed to generate 70% of training data
And 30% of the test data
S22, from the training data
Extract data of size m
Optimizing the convolution characteristic mapping network unit and the full-connection classification network unit in the convolution neural network model through the back propagation algorithm;
s23, testing data through the optimized convolutional neural network model
Predicting and calculating the accuracy, if the accuracy is less than the preset accuracy, executing the step S22And otherwise, stopping training and storing all the obtained parameters to generate the fault diagnosis model.
Further, in an embodiment of the present invention, the S3 further includes:
migrating the fault diagnosis model generated in step S2 based on the migration method of countermeasure learning by using the source domain data without a tag and the target domain data without a tag, exploring a complex feature space by using the Wasserstein distance-guided countermeasure network, narrowing distribution differences between different domain data features by a countermeasure training strategy to obtain domain invariant features, and finally implementing cross-domain migration.
Further, in an embodiment of the present invention, the step S3 and the step S4 specifically include:
step 1, training a data set X from the labeled source domainsWith the unlabeled target domain data set X to be testedtMiddle-extracting migration training data with size of m
And
step 2, optimizing the domain evaluation network D by using the back propagation algorithmjAnd a full junction layer Fcj：
Step 3, training data set X from the labeled source domainsExtracting m-sized micro-call training data
Step 4, optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network model by using the back propagation algorithm;
step 5, evaluating the network D in the current domainjThe output Wessenstein distance is less than a preset threshold ThrewStopping training, keeping the obtained parameters, and completing the fault diagnosis modulePerforming migration training and fine adjustment on the model; otherwise, returning to the step 1 and re-executing the steps 1-5.
Further, in an embodiment of the present invention, the step S3 further includes:
and the fault diagnosis model after the migration training realizes the mapping of the features through the deep convolutional neural network, the domain evaluation network guides the domain-invariant feature learning process, and when the distribution between the source domain training data and the target domain training data is not biased, the domain-invariant feature mapping is obtained.
In order to achieve the above object, another embodiment of the present invention provides a system for diagnosing mechanical failure migration based on countermeasure learning, including: the data generation module is used for acquiring original signals of mechanical faults of a sample to be tested under different working conditions, and analyzing the original signals of the mechanical faults under the different working conditions to generate a source domain training data set with a label, a source domain training data set without the label and a target domain testing data set without the label under the different working conditions;
the model generation module is used for training a deep convolutional neural network model according to the source domain training data set with the label and a back propagation algorithm to generate a fault diagnosis model;
a model training module for performing migration training on the fault diagnosis model according to the unlabeled source domain training dataset, the unlabeled target domain testing dataset, a migration method of countermeasure learning, and a Wasserstein distance-guided countermeasure network;
the model fine-tuning module is used for fine-tuning the fault diagnosis model after the migration training according to the source domain training data set with the label and a back propagation algorithm;
and the diagnosis module is used for inputting the target domain test data set without the label into the fault diagnosis model after fine adjustment and outputting the fault category of the sample to be tested.
According to the mechanical fault migration diagnosis system based on the countermeasure learning, the original signals of the mechanical faults under different working conditions are obtained, and source domain training data with labels and target domain test data without the labels are generated; optimizing a convolutional neural network serving as a basic diagnosis model by using a back propagation algorithm by using source domain training data with labels; obtaining a domain invariant feature by using source domain training data without a label and target domain test data through a Wasserstein distance-guided counterstudy method, and realizing cross-domain migration; the source domain training data with the labels are utilized, and a back propagation algorithm is used for fine tuning of the migration model, so that the problem of over-migration is avoided; and inputting the data of the label-free target domain to be tested into the migrated model to obtain a fault classification result.
In addition, the mechanical failure migration diagnosis system based on the countermeasure learning according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the model generation module includes: the device comprises a data distribution unit, an optimization unit and a determination unit;
the data distribution unit is used for distributing the labeled source domain training data set XsRandomly distributed to generate 70% of training data
And 30% of the test data
The optimization unit is used for obtaining training data
Extract data of size m
Optimizing the convolution characteristic mapping network unit and the full-connection classification network unit in the convolution neural network model through the back propagation algorithm;
the determining unit is used for testing data through the optimized convolutional neural network model
And predicting and calculating the accuracy, and stopping training and storing all the obtained parameters to generate the fault diagnosis model when the accuracy is greater than the preset accuracy.
Further, in one embodiment of the present invention, the model training module is further configured to,
migrating the generated fault diagnosis model based on the migration method of the countermeasure learning by using the source domain data without the label and the target domain data without the label, exploring a complex feature space by adopting the Wasserstein distance-guided countermeasure network, reducing distribution difference among different domain data features by a countermeasure training strategy to obtain domain invariant features, and finally realizing cross-domain migration.
Further, in one embodiment of the present invention, the model training module and the model fine tuning module are further configured to,
training a dataset X from the tagged source domainsWith the unlabeled target domain data set X to be testedtMiddle-extracting migration training data with size of m
And
and optimizing the domain evaluation network D using the back propagation algorithmjAnd a full junction layer Fcj：
Training a dataset X from the tagged source domainsExtracting m-sized micro-call training data
Optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network model by using the back propagation algorithm;
local domain evaluation network DjThe output Wessenstein distance is less than a preset threshold ThrewStopping training, keeping the obtained parameters, and completing the trainingAnd (5) migration training and fine adjustment of the fault diagnosis model.
Further, in an embodiment of the present invention, the model training module is further configured to,
and the fault diagnosis model after the migration training realizes the mapping of the features through the deep convolutional neural network, the domain evaluation network guides the domain-invariant feature learning process, and when the distribution between the source domain training data and the target domain training data is not biased, the domain-invariant feature mapping is obtained.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method for mechanical fault migration diagnosis based on countermeasure learning according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for mechanical fault migration diagnosis based on countermeasure learning according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an overview of a mechanical failure migration diagnostic model based on countermeasure learning, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating visualization of base model features for source domain training data in a D → B migration task according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating visualization of fundamental model features of target domain test data in a D → B migration task according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating visualization of migration model features for source domain training data in a D → B migration task according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating visualization of migration model features for target domain test data in a D → B migration task according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a mechanical failure migration diagnosis system based on countermeasure learning according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a mechanical failure migration diagnosis method and system based on countermeasure learning according to an embodiment of the present invention with reference to the drawings.
First, a proposed countermeasure learning-based mechanical failure migration diagnosis method according to an embodiment of the present invention will be described with reference to the drawings.
Fig. 1 is a flowchart of a method for diagnosing mechanical failure migration based on countermeasure learning according to an embodiment of the present invention.
As shown in fig. 1, the method for diagnosing mechanical fault migration based on countermeasure learning includes the following steps:
in step S1, original signals of mechanical faults of the sample to be tested under different conditions are obtained, and the original signals of mechanical faults under different conditions are analyzed to generate a source domain training data set with a label, a source domain training data set without a label, and a target domain testing data set without a label under different conditions.
Specifically, original signals of mechanical faults under different working conditions are obtained, and a labeled source domain training data set under the 1 st working condition is sorted and generated
And 2, target domain data set to be tested without label in working condition
Wherein
And
are respectively a data set XsAnd XtAnd the ith and jth samples of (1), each sample having n data points, msAnd mtRespectively the total number of samples of the two data sets,
is a data set XsThe fault category label of the ith sample of (1).
In step S2, a deep convolutional neural network model is trained according to the labeled source domain training data set and the back propagation algorithm, and a fault diagnosis model is generated.
In particular, utilizing tagged source domain training data
And training a deep convolutional neural network model constructed by the convolutional layer and the fully-connected layer by using a back propagation algorithm to obtain a fault diagnosis model capable of accurately classifying training data.
It will be appreciated that a convolutional neural network is a type of neural network that uses a convolutional calculation method instead of a general matrix calculation. A typical convolution calculation is for image classification, and a two-dimensional convolution is defined as:
for the problem of migration fault diagnosis of mechanical faults, 1-dimensional vibration signals are processed, and therefore the embodiment of the invention uses a one-dimensional convolution neural network. As is readily apparent from equation (1), when m is equal to 1, the two-dimensional convolution is converted to a one-dimensional convolution. The one-dimensional convolution calculation formula is as follows:
wherein the content of the first and second substances,
the first convolutional layer having a dimension n × 1 × j
The jth convolution kernel of (a);
is the signal segment of the ith input; bijIs a deviation; phi is the activation function.
For the fault classification problem of the embodiment of the invention, a fully-connected layer is added after the continuous convolutional layers to output the classification result, and the formula of the fully-connected layer is defined as follows:
yl＝φ(Wlyl-1+bl) (3)
wherein, WlIs a direct weight parameter of two fully-connected layers, yl-1Is a feature map of an upper layer, blIs the deviation of the current layer.
Training a fault-classified convolutional neural network using a back-propagation method, the classification network including convolutional feature mapping network elements M (with a parameter θ) for extracting multi-layer fault features of a data setM) And a fully connected classifying network unit C (with a parameter theta) for establishing a mapping between the fault signature and the sample signaturec) The loss of the model is defined as the cross entropy between the SoftMax function prediction probability distribution and the One-hot encoding of the source domain data tag:
wherein the content of the first and second substances,
is a function of the indication of the function,
is the predicted distribution value of the K-th dimension, and K is the number of classes.
Further, in an embodiment of the present invention, step S2 specifically includes:
s21, the source domain training data set X with labelssRandomly distributed to generate 70% of training data
And 30% of the test data
S22, from the training data
Extract data of size m
Optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network by using a back propagation algorithm, and updating the convolution neural network according to the following formula:
s23, testing data through the optimized convolutional neural network model
Predicting and calculating the accuracy, if the accuracy is less than the preset accuracy ThrecThen step S22 is executed, otherwise, the training is stopped and all the obtained parameters are saved, and the fault diagnosis model is generated.
In step S3, the fault diagnosis model is migration-trained according to the unlabeled source domain training dataset, the unlabeled target domain testing dataset, the migration method of the countermeasure learning, and the Wasserstein distance-guided countermeasure network.
Further, migration is performed on the basic diagnosis model obtained in the previous step by using source domain data and target domain data without labels based on a migration method of countermeasure learning, a complicated feature space is explored by adopting a Wasserstein distance-guided countermeasure network, domain invariant features are obtained by reducing distribution differences among different domain data features through a countermeasure training strategy, and cross-domain migration is finally achieved.
In step S4, the fault diagnosis model after the migration training is trimmed according to the labeled source domain training data set and the back propagation algorithm.
In particular, utilizing tagged source domain training data
And (3) fine-tuning the diagnosis model obtained after migration by using a back propagation algorithm, ensuring the applicability of the diagnosis model to a source domain data set and avoiding the problem of over-migration.
It is understood that the Wasserstein distance is a distributed measurement method. The Wasserstein metric is a metric for measuring the distance between data distributions in the Polish metric space (M, p). For the measurement of two different probability distributions P and Q, the Wasserstein distance formula is defined as follows:
in an embodiment of the present invention, a 1 st order Wasserstein distance function is used to guide the antagonistic network training process, the 1 st order Wasserstein distance function formula, satisfying the Kantorovich-Rubinstein duality, is as follows:
where the upper bound covers all 1-L ipschitz functions f: x → R.
The Wasserstein distance-guided countermeasure training strategy, the objective function of the countermeasure network used in the embodiment of the present invention is defined as follows:
wherein D is a 1-L ipschitz function, PgIs the distribution of generative models
z-p (z) to satisfy the L ipschitz constraint, a penalty term is added to the objective function to solve the optimization difficulty caused by the constraint.
λ is a penalty factor.
In the proposed migration fault diagnosis model, a deep convolutional neural network is used for realizing the mapping of the features, and a domain evaluation network is used for guiding the learning process of the domain invariant features. When the distribution between the source domain and the target domain is not biased, a domain-invariant feature map will be obtained. Meanwhile, the domain evaluator is used for estimating the Wasserstein distance of the feature distribution of the source domain and the target domain. Source domain feature distribution
And target domain feature distribution
Wasserstein distance of (a) by maximizing a loss function L of domain evaluation network DwdIs estimated, wherein fs＝F(xs) And ft＝F(xt). The loss function is defined as follows:
wherein x issAnd xtIs from the source domain XsAnd a target domain XtTo satisfy the L ipsitz constraint on Wasserstein distanceConditional, putting forward a penalty term for adding constraint condition to original target function
The formula is defined as follows:
wherein the content of the first and second substances,
is from distribution
Is sampled randomly.
And obtaining the domain-invariant feature through the training strategy learning. Firstly, training a domain evaluator network to maximize the Wasserstein distance of two domains, then, fixing the parameters of the domain evaluator network, and reducing the Wasserstein distance by adjusting the feature mapping in the depth model. The learning strategy formula of the domain invariant feature is expressed as follows:
where λ is a penalty coefficient.
Acquiring domain invariant features, Fc, by migrating fully-connected classification network units C before a source domain and a target domain for a convolutional neural network of a basic diagnostic modelj(parameters thereof)
) Is the jth fully-connected layer in a fully-connected network, and evaluates the network D using multiple domainsj(parameters thereof)
) To estimate the Wasserstein distance between the source domain and the target domain. Thus, the penalty function for the resistance training strategy is defined as:
wherein the content of the first and second substances,
is a gradient penalty term and λ is a penalty coefficient.
According to the proposed fine tuning of the migration model, in the process of domain invariant feature confrontation iterative training, after each confrontation network optimization adjustment, a source domain training data set is used, and a convolution feature mapping network unit M and a full-connection classification network unit C in a convolution neural network are adjusted through a back propagation algorithm of a formula (5) and a formula (6), so that the migration model is fine tuned.
Further, in an embodiment of the present invention, the steps S3 and S4 specifically include:
step 1, training data set X from source domain with labelsWith the unlabeled target domain data set X to be testedtMiddle-extracting migration training data with size of m
And
step 2, optimizing the domain evaluation network D by using a back propagation algorithmjAnd a full connection layer
And updating the network according to the following formula:
step 3, training data set X from the source domain with labelssExtracting m-sized micro-call training data
And 4, optimizing a convolution feature mapping network unit M and a fully-connected classification network unit in the convolution neural network model by using a back propagation algorithm to update the convolution neural network according to the following formula:
step 5, evaluating the network D in the current domainjThe output Wessenstein distance is less than a preset threshold ThrewStopping training, keeping the obtained parameters, and completing the migration training and fine adjustment of the fault diagnosis model; otherwise, returning to the step 1 and re-executing the steps 1-5.
In step S5, the unlabeled target domain test data set is input into the trimmed fault diagnosis model, and the fault category of the sample to be tested is output.
Specifically, target domain data to be tested
And inputting the migrated diagnosis model to obtain the fault category of the test sample.
The present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
An overall scheme of a mechanical fault migration diagnosis model based on countermeasure learning under variable working conditions is shown in FIG. 2. Using a deep convolutional neural network as a basic diagnostic model, including multiple convolutional layers, a fully-connected layer, and a final Softmax output layer, a source domain training dataset X is utilizedsComputing the cross entropy of the model prediction output and the data labels, optimizing the underlying diagnostics by back-propagation algorithmAnd the model is subjected to iterative training to achieve the target that the source domain training data can be accurately classified. On the basis of a basic model, a countermeasure training strategy is used for learning domain invariant features in each layer of features in a depth model respectively, a domain evaluation network is used for evaluating Wasserstein distance of feature distribution of a source domain and a target domain, and the distribution distance across the domains is reduced step by step through iterative training. The method aims to solve the problem of migration caused by incomplete data in the practical application of fault diagnosis by adjusting and learning the feature distribution of target domain test data to be close to the feature distribution of source domain data through an antagonistic learning strategy on the basis of accurately classifying the source domain training data. Therefore, after the Wasserstein distance of different domains is reduced by the countertraining of each generation, the migration model is finely adjusted by using the labeled source domain data, the applicability of the migration model to the source domain data is ensured, and the problem of over-migration is avoided.
Taking a rolling bearing fault diagnosis task under 4 different working conditions as an example, the mechanical fault migration diagnosis method based on the countermeasure network is verified. Under the load conditions of 4 different driving motors, vibration signals of the bearings are collected through a vibration sensor, and the sampling frequency is set to be 12 kHz. The fault database comprises 3 faults of inner ring defects, outer ring defects and rolling body defects, wherein each fault has 3 fault sizes, 10 types of faults are added in a normal state, 500 sample data are provided for each type, and specifically shown in table 1, and table 1 is a bearing fault data set description under different working conditions.
TABLE 1
According to the framework provided by the embodiment of the invention, for the bearing fault diagnosis problem, as shown in fig. 3, a convolutional neural network is used for constructing a basic diagnosis model, the framework and the detailed information of the convolutional neural network are shown in table 2, and table 2 is the detailed information of the convolutional neural network model. Using a domain evaluation network to estimate the Wasserstein distance between the cross-domain distributions of each layer, the domain detail information of the domain evaluation network is shown in table 3, and table 3 is the domain evaluation network detail information.
TABLE 2
TABLE 3
Training a convolutional neural network of basic diagnosis:
step 1: training data set X of source domainsRandomly distributed to obtain 70% of training data
And 30% of the test data
Step 2: from training data
Extract data of size m
And step 3: optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network by using a back propagation algorithm, and updating the convolution neural network according to the following formula:
and 4, step 4: using convolutionNeural network pair test data
Predicting and calculating the accuracy, and when the accuracy is less than a set threshold ThrecReturning to the step 2, and re-executing the steps 2-4; otherwise, stopping training and storing all the obtained parameters, and completing the pre-training of the basic diagnosis model.
The method comprises the following steps of confrontation training and fine tuning of a migration model:
step 1: training dataset X from Source DomainsTesting data set X with target domaintMiddle-extracting migration training data with size of m
And
step 2: optimizing domain evaluation network D using back-propagation algorithmjAnd a full junction layer FcjThe network is updated according to the following formula:
and step 3: training dataset X from Source DomainsExtracting m-sized micro-call training data
And 4, step 4: updating the convolutional neural network by using the convolutional characteristic mapping network unit M and the fully-connected classification network unit C optimized by the back propagation algorithm according to the following formula:
and 5: when the Wessestein distance output by the domain evaluation network is smaller than a set threshold ThrewStopping training and keeping the obtained parameters to finish the training and fine adjustment of the migration model; otherwise, returning to the step 1 and re-executing the steps 1-5.
The hyper-parameters in the training process are set as learning rate α being 0.0001, mini-batch size being m being 64, penalty coefficient lambda being 10, basic diagnosis model test accuracy threshold Threc99%, Wasserstein distance threshold, Threw＝0.0001。
4 different types of working condition data sets are designed to be migrated mutually to verify the method of the embodiment. E.g., A → B, with data set A as the source domain training data set and data set B as the target domain testing data set. The comparative calculation results are shown in table 4, and table 4 shows the diagnosis accuracy of the basic model and the migration model in different migration tasks. The result shows that the migration method of the embodiment of the invention can greatly improve the diagnosis accuracy of the basic model to the target domain test data.
| A→B | A→C | A→D | B→A | B→C | B→D | |
| Basic model | 87.93％ | 89.00％ | 80.73％ | 97.47％ | 99.40％ | 89.00％ |
| Migration model | 99.73％ | 99.67％ | 100.00％ | 99.13％ | 100.00％ | 99.93％ |
| C→A | C→B | C→D | D→A | D→B | D→C | |
| Basic model | 97.00％ | 97.20％ | 89.53％ | 90.20％ | 75.53％ | 79.26％ |
| Transfer moldModel (III) | 98.53％ | 99.80％ | 100.00％ | 98.07％ | 98.27％ | 99.53％ |
FIGS. 4-7 are graphs showing the results of visualization of the diagnostic model domain invariant feature in the D → B migration task using s-TNE techniques. Fig. 4 and 5 are respectively the visualization results of the characteristics of the basic model on the source domain training data and the target domain test data, and fig. 6 and 7 are respectively the visualization results of the migration model on the source domain training data and the target domain test data. The result shows that the difference of the characteristic distribution of the basic diagnosis model of the target test data and the source domain training data is obvious, particularly the categories 7, 8 and 9 are most prominent, the spatial distance of the characteristic distribution is very large, and the basic model is obviously difficult to accurately classify and identify the target domain test data. After the migration is carried out by the method of the embodiment of the invention, the distribution distances of all the categories in the feature space are basically consistent, so that the model after the migration can accurately classify and diagnose the source domain training data and the target domain test data.
According to the mechanical fault migration diagnosis method based on the countermeasure learning, provided by the embodiment of the invention, the original signals of the mechanical fault under different working conditions are obtained, and the source domain training data with the label and the target domain test data without the label are generated; optimizing a convolutional neural network serving as a basic diagnosis model by using a back propagation algorithm by using source domain training data with labels; obtaining a domain invariant feature by using source domain training data without a label and target domain test data through a Wasserstein distance-guided counterstudy method, and realizing cross-domain migration; the source domain training data with the labels are utilized, and a back propagation algorithm is used for fine tuning of the migration model, so that the problem of over-migration is avoided; and inputting the data of the label-free target domain to be tested into the migrated model to obtain a fault classification result.
Next, a mechanical failure migration diagnosis system based on countermeasure learning proposed according to an embodiment of the present invention is described with reference to the drawings.
Fig. 8 is a schematic structural diagram of a mechanical failure migration diagnosis system based on countermeasure learning according to an embodiment of the present invention.
As shown in fig. 8, the countermeasure learning-based mechanical failure migration diagnosis system includes: data generation module 100, model generation module 200, model training module 300, model fine tuning module 400, and diagnostic module 500.
The data generating module 100 is configured to obtain original signals of mechanical faults of a sample to be tested under different working conditions, and analyze the original signals of the mechanical faults under the different working conditions to generate a source domain training data set with a label, a source domain training data set without the label, and a target domain testing data set without the label under the different working conditions.
The model generation module 200 is configured to train a deep convolutional neural network model according to the labeled source domain training data set and the back propagation algorithm, and generate a fault diagnosis model.
The model training module 300 is used for performing migration training on the fault diagnosis model according to the unlabeled source domain training data set, the unlabeled target domain testing data set, the migration method of the countermeasure learning and the Wasserstein distance-guided countermeasure network.
The model fine-tuning module 400 is configured to fine-tune the fault diagnosis model after the migration training according to the labeled source domain training data set and the back propagation algorithm.
The diagnosis module 500 is configured to input the target domain test data set without the label into the trimmed fault diagnosis model, and output the fault category of the sample to be tested.
The mechanical fault migration diagnosis system 10 based on the countermeasure learning obtains the domain invariant feature through the countermeasure learning method, thereby realizing the migration before different domains and realizing the intelligent diagnosis of the variable working condition mechanical fault.
Further, in one embodiment of the present invention, the model generation module includes: the device comprises a data distribution unit, an optimization unit and a determination unit;
a data distribution unit for distributing the labeled source domain training data set XsRandomly distributed to generate 70% of training data
And 30% of the test data
An optimization unit for deriving training data
Extract data of size m
Optimizing a convolution characteristic mapping network unit and a full-connection classification network unit in the convolution neural network model through a back propagation algorithm;
a determination unit for determining the test data by the optimized convolutional neural network model
And predicting and calculating the accuracy, and stopping training and storing all the obtained parameters to generate a fault diagnosis model when the accuracy is greater than the preset accuracy.
Further, in one embodiment of the present invention, the model training module is further configured to,
the method comprises the steps of migrating a generated fault diagnosis model by using source domain data without a label and target domain data without a label based on a migration method of countermeasure learning, exploring a complex feature space by using a Wasserstein distance-guided countermeasure network, reducing distribution difference among different domain data features by using a countermeasure training strategy to obtain domain invariant features, and finally realizing cross-domain migration.
Further, in one embodiment of the present invention, the model training module and the model fine tuning module are further configured to,
slave beltSource domain training dataset X of labelssWith the unlabeled target domain data set X to be testedtMiddle-extracting migration training data with size of m
And
and optimizing the domain evaluation network D using a back propagation algorithmjAnd a full junction layer Fcj：
Training dataset X from tagged source domainsExtracting m-sized micro-call training data
Optimizing a convolution characteristic mapping network unit M and a full-connection classification network unit C in the convolution neural network model by using a back propagation algorithm;
local domain evaluation network DjThe output Wessenstein distance is less than a preset threshold ThrewAnd stopping training, keeping the obtained parameters, and completing the migration training and fine adjustment of the fault diagnosis model.
Further, in one embodiment of the present invention, the model training module is further configured to,
and the fault diagnosis model after the migration training realizes the mapping of the features through the deep convolutional neural network, the domain evaluation network guides the domain-invariant feature learning process, and when the distribution between the source domain training data and the target domain training data is not biased, the domain-invariant feature mapping is obtained.
It should be noted that the foregoing explanation of the embodiment of the mechanical fault migration diagnosis method based on countermeasure learning also applies to the system of this embodiment, and details are not described here.
According to the mechanical fault migration diagnosis system based on the countermeasure learning, provided by the embodiment of the invention, the original signals of the mechanical faults under different working conditions are obtained, and the source domain training data with the labels and the target domain test data without the labels are generated; optimizing a convolutional neural network serving as a basic diagnosis model by using a back propagation algorithm by using source domain training data with labels; obtaining a domain invariant feature by using source domain training data without a label and target domain test data through a Wasserstein distance-guided counterstudy method, and realizing cross-domain migration; the source domain training data with the labels are utilized, and a back propagation algorithm is used for fine tuning of the migration model, so that the problem of over-migration is avoided; and inputting the data of the label-free target domain to be tested into the migrated model to obtain a fault classification result.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A mechanical fault migration diagnosis method based on countermeasure learning is characterized by comprising the following steps:
s1, acquiring original signals of mechanical faults of a sample to be tested under different working conditions, and analyzing the original signals of the mechanical faults under different working conditions to generate a source domain training data set with a label, a source domain training data set without the label and a target domain testing data set without the label under different working conditions;
s2, training a deep convolutional neural network model according to the labeled source domain training data set and the back propagation algorithm, and generating a fault diagnosis model;
s3, performing migration training on the fault diagnosis model according to the unlabeled source domain training data set, the unlabeled target domain testing data set, a migration method of countermeasure learning and a Wasserstein distance-guided countermeasure network;
s4, fine-tuning the fault diagnosis model after migration training according to the source domain training data set with the label and a back propagation algorithm;
and S5, inputting the target domain test data set without the label into the fault diagnosis model after fine adjustment, and outputting the fault category of the sample to be tested.
2. The method according to claim 1, wherein the S2 further comprises:
s21, the labeled source domain training data set X is processedsRandomly distributed to generate 70% of training data
And 30% of the test data
S22, from the training data
Extract data of size m
Optimizing the convolution characteristic mapping network unit and the full-connection classification network unit in the convolution neural network model through the back propagation algorithm;
s23, testing data through the optimized convolutional neural network model
And predicting and calculating the accuracy, executing the step S22 if the accuracy is less than the preset accuracy, otherwise, stopping training and storing all the obtained parameters, and generating the fault diagnosis model.
3. The method according to claim 1, wherein the S3 further comprises:
migrating the fault diagnosis model generated in the step S2 based on the migration method of the countermeasure learning by using the source domain training dataset without a label and the target domain test dataset without a label, exploring a complex feature space by using the Wasserstein distance-guided countermeasure network, reducing distribution differences among different domain data features by using a countermeasure training strategy to obtain domain invariant features, and finally implementing cross-domain migration.
4. The method according to claim 1, wherein the steps S3 and S4 specifically include:
step 1, training a data set X from the labeled source domainsTesting the data set X with the unlabeled target domaintMiddle-extracting migration training data with size of m
And
step 2, using the reverse transmissionBroadcast algorithm optimization domain evaluation network DjAnd a full junction layer Fcj：
Step 3, training data set X from the labeled source domainsExtracting m-sized micro-call training data
Step 4, optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network model by using the back propagation algorithm;
step 5, evaluating the network D in the current domainjThe output Wessenstein distance is less than a preset threshold ThrewStopping training, keeping the obtained parameters, and completing the migration training and fine adjustment of the fault diagnosis model; otherwise, returning to the step 1 and re-executing the steps 1-5.
5. The method according to claim 1, wherein the step S3 further comprises:
and the fault diagnosis model after the migration training realizes the mapping of the features through the deep convolutional neural network, the domain evaluation network guides the domain-invariant feature learning process, and when the distribution between the source domain training data and the target domain training data is not biased, the domain-invariant feature mapping is obtained.
6. A system for mechanical fault migration diagnosis based on countermeasure learning, comprising:
the data generation module is used for acquiring original signals of mechanical faults of a sample to be tested under different working conditions, and analyzing the original signals of the mechanical faults under the different working conditions to generate a source domain training data set with a label, a source domain training data set without the label and a target domain testing data set without the label under the different working conditions;
the model generation module is used for training a deep convolutional neural network model according to the source domain training data set with the label and a back propagation algorithm to generate a fault diagnosis model;
a model training module for performing migration training on the fault diagnosis model according to the unlabeled source domain training dataset, the unlabeled target domain testing dataset, a migration method of countermeasure learning, and a Wasserstein distance-guided countermeasure network;
the model fine-tuning module is used for fine-tuning the fault diagnosis model after the migration training according to the source domain training data set with the label and a back propagation algorithm;
and the diagnosis module is used for inputting the target domain test data set without the label into the fault diagnosis model after fine adjustment and outputting the fault category of the sample to be tested.
7. The system of claim 6, wherein the model generation module comprises: the device comprises a data distribution unit, an optimization unit and a determination unit;
the data distribution unit is used for distributing the labeled source domain training data set XsRandomly distributed to generate 70% of training data
And 30% of the test data
The optimization unit is used for obtaining training data
Extract data of size m
Optimizing the convolution characteristic mapping network unit and the full-connection classification network unit in the convolution neural network model through the back propagation algorithm;
the determining unit is used for testing data through the optimized convolutional neural network model
And predicting and calculating the accuracy, and stopping training and storing all the obtained parameters to generate the fault diagnosis model when the accuracy is greater than the preset accuracy.
8. The system of claim 6, wherein the model training module is further configured to,
migrating the generated fault diagnosis model by using the source domain training data set without the label and the target domain testing data set without the label based on the migration method of the countermeasure learning, exploring a complex feature space by using the Wasserstein distance-guided countermeasure network, reducing distribution difference among different domain data features by using a countermeasure training strategy to obtain domain invariant features, and finally realizing cross-domain migration.
9. The system of claim 6, wherein the model training module and the model fine tuning module are further configured to,
training a dataset X from the tagged source domainsTesting the data set X with the unlabeled target domaintMiddle-extracting migration training data with size of m
And
and optimizing the domain evaluation network D using the back propagation algorithmjAnd a full junction layer Fcj：
Training a dataset X from the tagged source domainsExtracting m-sized micro-call training data
Optimizing a convolution feature mapping network unit M and a full-connection classification network unit C in the convolution neural network model by using the back propagation algorithm;
local domain evaluation network DjThe output Wessenstein distance is less than a preset threshold ThrewAnd stopping training, keeping the obtained parameters, and completing the migration training and fine adjustment of the fault diagnosis model.
10. The system of claim 6, wherein the model training module is further configured to,
and the fault diagnosis model after the migration training realizes the mapping of the features through the deep convolutional neural network, the domain evaluation network guides the domain-invariant feature learning process, and when the distribution between the source domain training data and the target domain training data is not biased, the domain-invariant feature mapping is obtained.
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