CN116756483B - Mechanical fault diagnosis method, device and equipment under condition that target working condition data are unavailable - Google Patents
Mechanical fault diagnosis method, device and equipment under condition that target working condition data are unavailable Download PDFInfo
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Abstract
The invention relates to a mechanical fault diagnosis method under the condition that target working condition data are unavailable, which comprises the steps of intercepting an acquired mechanical vibration time domain signal, unifying sample lengths and carrying out amplitude normalization to obtain a data set, and dividing the data set into a multi-source domain data set and a target domain data set; constructing inter-domain invariant representation learning network branches, and extracting inter-domain invariant features; constructing domain invariant representation learning network branches, and extracting domain invariant features; constructing a fusion classifier, and predicting fault class labels of the fusion classifier after fusion of the inter-domain invariant features and the intra-domain invariant features; constructing a fault diagnosis training model comprising inter-domain invariable representation learning network branches and intra-domain invariable representation learning network branches and fusion classifiers; inputting samples in the multi-source domain data set, and performing model training by using a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model; and inputting the samples in the target domain data set, and obtaining the fault types of the samples.
Description
Technical Field
The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method, device and equipment under the condition that target working condition data are unavailable.
Background
The effective fault monitoring and diagnosing technology has very important significance for ensuring the safe operation of mechanical equipment, and with the rapid development of deep learning, the intelligent fault diagnosing method based on the data driving technology is a hotspot widely studied by students at present. The fault diagnosis method based on the deep neural network has remarkable advantages compared with other methods when the fault classification problem is processed, can learn more discriminative effective characteristics from the original data, and is an end-to-end fault diagnosis method. However, the working conditions of the mechanical parts are often changed, such as rotation speed change and load change, the probability distribution of measured data is greatly changed under different working conditions, and the diagnosis performance is greatly limited when the deep learning method faces to complex working condition changes.
With the introduction of transfer learning into the field of fault diagnosis, the fault diagnosis method based on deep transfer learning is widely applied to mechanical fault diagnosis under variable working conditions. In order to extract the features with more migration capability, the countermeasure ideas are introduced from the generation of the countermeasure network, and the countermeasure ideas are combined with the deep neural network to form a domain countermeasure fault diagnosis model. The fault diagnosis models require that source domain working condition data and target domain working condition data participate in model training at the same time, and the labeled source domain and the unlabeled target domain are subjected to data distribution alignment through feature mapping.
However, the working conditions of the mechanical system are many, and we cannot obtain monitoring data under all working conditions in advance, when the target working condition data is not available in the model training stage, the conventional domain anti-fault diagnosis model can fail catastrophically, because prior distribution of the target domain data is usually required to be learned in model training, so that alignment of data distribution is realized. When no target working condition data participates in model training, domain invariable movable characteristics with strong adaptability to working condition change can be extracted from a plurality of source domain working condition data in order to realize accurate fault diagnosis under the target working condition. At this time, one set of source domain data contains a complete tag, while the other sets of source domain data do not contain tags, so that labor and time costs caused by marking data can be reduced. The source domain without labels is called the auxiliary source domain.
The existing fault diagnosis model under unavailable target working condition data generally performs characteristic data distribution alignment through characteristic mapping or countermeasure training from a marked source domain and a plurality of unmarked auxiliary source domain data sets, trains the fault diagnosis model to learn a representation irrelevant to distribution, acquires cross-domain migration knowledge, and extracts inter-domain invariant features. The fault diagnosis model aims at carrying out data distribution alignment on a plurality of source domains, so that generalization and robustness of the model are improved. Firstly, a marked source domain and an unmarked auxiliary source domain data set are input into a network to perform model parameter training, after training is completed, target domain test data which does not participate in training is input into an optimized model to perform fault identification, and a fault type is output.
However, the existing fault diagnosis model under the condition that the target working condition data is not available is more focused on the alignment of data distribution to learn cross-domain distribution knowledge, and the inter-domain invariant features are extracted without considering the intra-domain data attributes. Some current studies indicate that simple data distribution alignment alone may compromise the resolution of the model as well as the diversity and sufficiency of features. The intra-domain invariant feature mainly captures the intrinsic semantic information of the data, is generated inside the domain and is not influenced by other domains, is useful for improving the performance of the model, and should not be ignored. Therefore, the existing failure diagnosis model under the condition that the target working condition data is not available has the following disadvantages: ignoring domain invariant feature extraction, domain invariant fault feature extraction is insufficient; the fault diagnosis accuracy is not high; the generalization capability for unavailable target operating mode data is weak.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to solve the problems of insufficient extraction of domain-invariant fault characteristics and low fault diagnosis accuracy in the prior art.
In order to solve the technical problems, the invention provides a mechanical fault diagnosis method under the condition that target working condition data is unavailable, comprising the following steps:
S1, intercepting an acquired mechanical vibration time domain signal, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
s2, constructing an inter-domain invariable representation learning network branch based on the first feature extractor, the first auxiliary classifier and the auxiliary discriminator, and extracting inter-domain invariable features of an input sample;
s3, constructing a domain invariant representation learning network branch based on the fast Fourier transform module, the second feature extractor and the second auxiliary classifier, wherein the domain invariant representation learning network branch is used for extracting domain invariant features of an input sample;
S4, constructing a fusion classifier based on the full connection layer and the Softmax classifier, and fusing the inter-domain invariant features and the intra-domain invariant features and predicting fault class labels of the fused features;
S5, constructing a fault diagnosis training model comprising the inter-domain invariable representation learning network branch, the intra-domain invariable representation learning network branch and the fusion classifier;
s6, inputting samples in the multi-source domain data set into the fault diagnosis training model, and performing model training by using a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model;
s7, inputting the samples in the target domain data set into a trained fault diagnosis training model, and obtaining fault types of the samples.
In one embodiment of the invention, the data set is divided according to different working conditions of the machine, the same rotating speed and load are the same working condition, and multiple health state samples under the same working condition are placed in the same domain, and the types of the health states of the machine contained in different domains are the same; the multi-source domain dataset comprises a marked source domain and a plurality of unmarked auxiliary source domains, which are used for training a model; the target domain dataset is not available during the model training phase and does not participate in model training.
In one embodiment of the present invention, in step S6, the training process of the fault diagnosis training model includes:
In the inter-domain invariant representation learning network branch, minimizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the auxiliary discriminant D, maximizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the first feature extractor F1 for sample data from the labeled source domain and the unlabeled auxiliary source domain; minimizing the cross entropy loss L s1 of the first auxiliary classifier C1 to optimize the first auxiliary classifier C1 and the first feature extractor F1 for the labeled sample data from the source domain;
Performing a fast fourier transform on the labeled sample data from the source domain in the domain invariant representation learning network branch to minimize the cross entropy penalty L s2 of the second auxiliary classifier C2 to optimize the second auxiliary classifier C2 and the second feature extractor F2;
in the fusion classifier FC, minimizing cross entropy loss L s3 of the fusion classifier FC for labeled sample data from the multi-source domain dataset to optimize the fusion classifier FC, the first feature extractor F1, and the second feature extractor F2; the KL divergence loss between the integrated soft distribution (z 1+z2)/2 of the soft distribution z 2 output by the first auxiliary classifier C1 and the soft distribution z 1 output by the second auxiliary classifier C2 and the soft distribution z f output by the fusion classifier FC is minimized through a mutual learning strategy, and classification knowledge of the inter-domain invariable representation learning network branches and the intra-domain invariable representation learning network branches is distilled into the fusion classifier; respectively minimizing KL divergence loss between the fusion classifier output soft distribution z f and the first auxiliary classifier C1 output soft distribution z 1 and the second auxiliary classifier C2 output soft distribution z 2, and respectively distilling knowledge of the fusion classifier into an inter-domain invariable representation learning network branch and an intra-domain invariable representation learning network branch; maximizing Euclidean distance loss from inter-domain invariant features extracted from the labeled source domain to intra-domain invariant features through a feature difference maximization strategy; and setting fixed iterative training times, stopping training until the fault diagnosis training model is stable, and outputting a trained fault diagnosis training model.
In one embodiment of the present invention, in step S2, extracting the inter-domain invariant feature of the input sample includes:
the first feature extractor F1 takes a marked source domain and an unmarked auxiliary source domain as inputs and outputs high-level implicit features;
The first auxiliary classifier C1 and the auxiliary discriminator D take the high-level implicit features extracted by the first feature extractor F1 as inputs, and output fault class labels and domain class labels respectively, wherein the high-level implicit features extracted by the first feature extractor F1 are inter-domain invariant features.
In one embodiment of the present invention, in step S3, extracting the intra-domain invariant feature of the input sample includes:
The fast Fourier transform module takes a mechanical vibration time domain signal in a marked source domain as input, converts the mechanical vibration time domain signal into a mechanical vibration frequency domain signal, analyzes the frequency spectrum of the mechanical vibration frequency domain signal, and acquires frequency domain characteristics;
The second feature extractor F2 takes the mechanical vibration frequency domain signal as input and outputs high-level implicit features;
the second auxiliary classifier C2 takes the high-level hidden feature extracted by the second feature extractor F2 as input, outputs a fault class label, and the high-level hidden feature extracted by the second feature extractor F2 is an intra-domain invariant feature.
In one embodiment of the invention, the first feature extractor and the second feature extractor each include, but are not limited to, being constructed using one of a fully connected network, a deep convolutional network, a deep confidence network, and a deep residual network;
the first auxiliary classifier and the second auxiliary classifier both comprise a full connection layer and a Softmax classifier, and the label classification loss of the Softmax classifier is the cross entropy loss of the sample prediction class label in the labeled source domain;
The auxiliary discriminator comprises a full-connection layer and a Sigmoid activation function, and the domain discrimination loss of the auxiliary discriminator is a binary cross entropy loss of a sample prediction domain label in the multi-source domain data set.
In one embodiment of the present invention, in step S4, the inter-domain invariant feature and the intra-domain invariant feature are fused by adding corresponding elements in the feature vector.
In one embodiment of the present invention, in step S6, the mutual learning strategy is to use KL divergence loss Kullback-Leibler to exchange information with each other when training the inter-domain invariant representation learning network branch, the intra-domain invariant representation learning network branch and the fusion classifier; the feature difference maximization strategy reduces the repetition and redundancy of features by maximizing the Euclidean distance between the inter-domain invariant features and the intra-domain invariant features; the optimization algorithm includes, but is not limited to, one of a root mean square transfer algorithm, a random gradient descent algorithm, and an adaptive moment estimation algorithm.
The embodiment of the invention also provides a mechanical fault diagnosis device under the condition that the target working condition data is unavailable, comprising:
the data preprocessing module is used for intercepting the acquired mechanical vibration time domain signals, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
The inter-domain invariable representation learning network branch construction module is used for constructing inter-domain invariable representation learning network branches based on the first feature extractor, the first auxiliary classifier and the auxiliary discriminator and extracting inter-domain invariable features of input samples;
The intra-domain invariant representation learning network branch construction module is used for constructing intra-domain invariant representation learning network branches based on the fast Fourier transform module, the second feature extractor and the second auxiliary classifier and extracting intra-domain invariant features of the input samples;
the fusion classifier construction module is used for constructing a fusion classifier based on the full connection layer and the Softmax classifier, fusing the inter-domain invariant features and the intra-domain invariant features, and predicting fault class labels of the fused features;
The fault diagnosis training model construction module is used for constructing a fault diagnosis training model comprising the inter-domain invariable representation learning network branches, the intra-domain invariable representation learning network branches and the fusion classifier;
the fault diagnosis training model training module is used for inputting samples in the multi-source domain data set into the fault diagnosis training model, and performing model training by using a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model;
and the fault diagnosis module is used for inputting the samples in the target domain data set into a trained fault diagnosis training model to obtain fault categories of the samples.
The embodiment of the invention also provides a mechanical fault diagnosis device under the condition that the target working condition data is unavailable, comprising:
a memory for storing a computer program;
And the processor is used for realizing the steps of the mechanical fault diagnosis method under the condition that the target working condition data is unavailable when the computer program is executed.
Compared with the prior art, the technical scheme of the invention has the following advantages:
According to the mechanical fault diagnosis method under the condition that target working condition data are unavailable, inter-domain invariant representation learning network branches and intra-domain invariant representation learning network branches are utilized to extract inter-domain invariant features and intra-domain invariant features of sample data, and a fusion classifier is utilized to fuse the inter-domain invariant features and the intra-domain invariant features, so that mobility and class discrimination of extracted features are improved; in the model training stage, the inter-domain invariable representation learning network branches and the intra-domain invariable representation learning network branches and the fusion classifier are mutually learned by utilizing a mutual learning strategy, so that the improvement of the overall performance of the model is promoted, and the fault diagnosis accuracy is improved; by using a feature difference maximization strategy, the repetition and redundancy of extracted features are reduced by maximizing Euclidean distance between inter-domain invariant features and intra-domain invariant features, feature diversification is promoted, the generalization capability on unavailable target working condition data is strong, and the application range is wide; the generalization capability of the model is remarkably improved, and the accurate diagnosis of mechanical faults under the target working condition without participating in model training is realized.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings, in which
FIG. 1 is a flow chart of steps of a method for diagnosing a mechanical failure in the event that target operating condition data is unavailable, provided by the present invention;
FIG. 2 is a schematic diagram of a training process of a fault diagnosis training model provided by the invention;
FIG. 3 is a schematic diagram of a diagnostic process of the fault diagnosis training model provided by the present invention;
FIG. 4 is a schematic diagram of a visual clustering result of high-level implicit features of a source domain and a target domain extracted by a fault diagnosis training model provided by the invention;
FIG. 5 is a schematic diagram of a confusion matrix of target domain bearing health results predicted by the fault diagnosis training model provided by the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
Referring to fig. 1, a flow chart of steps of a mechanical fault diagnosis method under the condition that target working condition data are unavailable according to the invention specifically includes:
s101, intercepting an acquired mechanical vibration time domain signal, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
The data sets are divided according to different working conditions of the machine, the same rotating speed and the same load are the same working condition, and multiple health state samples under the same working condition are placed in the same domain, and the types of the health states of the machine contained in different domains are the same. The multi-source domain data set comprises a marked source domain and a plurality of unmarked auxiliary source domains, and is used for training a model; the target domain dataset is not available during the model training phase and does not participate in model training.
S102, constructing an inter-domain invariant representation learning network branch based on a first feature extractor, a first auxiliary classifier and an auxiliary discriminator, and extracting inter-domain invariant features of an input sample;
S103, constructing a domain invariant representation learning network branch based on the fast Fourier transform module, the second feature extractor and the second auxiliary classifier, wherein the domain invariant representation learning network branch is used for extracting domain invariant features of an input sample;
S104, constructing a fusion classifier based on a full connection layer and a Softmax classifier, and fusing the inter-domain invariant features and the intra-domain invariant features and predicting fault class labels of the fused features;
s105, constructing a fault diagnosis training model comprising the inter-domain invariable representation learning network branch, the intra-domain invariable representation learning network branch and the fusion classifier;
The inter-domain invariant representation learning network branches and the intra-domain invariant representation learning network branches are in parallel structures and are used for extracting inter-domain invariant features and intra-domain invariant features respectively.
S106, inputting samples in the multi-source domain data set into the fault diagnosis training model, and performing model training by using a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model;
S107, inputting the samples in the target domain data set into a trained fault diagnosis training model to obtain fault categories of the samples.
Specifically, referring to fig. 2, a schematic structural diagram of a fault diagnosis training model of the present invention is shown; the first feature extractor F1 takes a marked source domain and an unmarked auxiliary source domain as inputs and outputs high-level implicit features; the first auxiliary classifier C1 and the auxiliary discriminator D take the high-level implicit features extracted by the first feature extractor F1 as inputs and respectively output fault class labels and domain class labels, so that the purpose is to learn cross-domain migration knowledge in a traditional domain countermeasure neural network DANN training mode, and the high-level implicit features extracted by the first feature extractor F1 are used as inter-domain unchanged features. The fast fourier transform FFT module takes as input a time domain signal in the source domain of the signature, it converts the acquired vibration signal from the time domain to the frequency domain, and obtains a frequency spectrum to help analyze the frequency domain characteristics of the signal. The frequencies and corresponding amplitudes in the vibration signal spectrum are used to analyze the characteristic frequencies corresponding to the fault categories. The fourier spectrum is less affected by domain shifts and is more representative of the fault class from which more information is extracted that contributes to classification. The second feature extractor F2 takes the converted frequency domain signal as input and outputs a high-level implicit feature. The second auxiliary classifier C2 takes the high-level implicit features extracted by the second feature extractor F2 as input and outputs fault class labels, so as to learn knowledge useful for fault classification in a traditional convolutional neural network CNN training manner, and takes the high-level implicit features extracted by the second feature extractor F2 as intra-domain invariant features. The fusion classifier FC is composed of a full connection layer and a Softmax classifier, and takes as input the fusion feature of the inter-domain invariant feature output by the first feature extractor F1 and the intra-domain invariant feature output by the second feature extractor F2, and takes as output the predicted sample fault class label.
Wherein the first feature extractor F1 includes, but is not limited to, being constructed by one of a fully connected network, a deep convolutional network, a deep confidence network, a deep residual network; the first auxiliary classifier C1 consists of a full connection layer and a Softmax classifier, and the label classification loss is a cross entropy loss L s1 of a sample prediction class label in a labeled source domain; the auxiliary discriminator D consists of a full connection layer and a Sigmoid activation function, and the domain discrimination loss is a binary cross entropy loss L adv of a sample prediction domain label in a multi-source domain. The second feature extractor F2 includes, but is not limited to, being constructed by one of a fully connected network, a deep convolutional network, a deep belief network, a deep residual network; the second auxiliary classifier C2 consists of a full-connected layer and Softmax classifier, whose label classification penalty is the cross entropy penalty L s2 of the sample prediction class labels in the labeled source domain. The fusion mode of the fusion classifier FC to the inter-domain invariant features and intra-domain invariant features is that corresponding elements in the feature vectors are added; the label classification penalty of the fusion classifier FC is the cross entropy penalty L s3 of the sample prediction class labels in the labeled source domain.
Specifically, in one embodiment of the present invention, a network fusion module of a fault diagnosis training model is formed by using the first feature extractor F1, the second feature extractor F2 and the fusion classifier FC, and is used for fusing and classifying the extracted inter-domain invariant features and intra-domain invariant features, and predicting fault class labels of input data samples.
Specifically, in step S106, the training process of the failure diagnosis training model includes:
S106-1: in the inter-domain invariant representation learning network branch, minimizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the auxiliary discriminant D, maximizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the first feature extractor F1 for sample data from the labeled source domain and the unlabeled auxiliary source domain; for labeled sample data from the source domain, the cross entropy loss L s1 of the first auxiliary classifier C1 is minimized to optimize the first auxiliary classifier C1 and the first feature extractor F1.
S106-2: in the intra-domain invariant representation learning network branch, labeled sample data from the source domain is subjected to a fast fourier transform to minimize the cross entropy loss L s2 of the second auxiliary classifier C2 to optimize the second auxiliary classifier C2 and the second feature extractor F2.
S106-3: in the network fusion module, minimizing cross entropy loss L s3 of the fusion classifier FC for labeled sample data from the source domain to optimize the fusion classifier FC, the first feature extractor F1, and the second feature extractor F2; then, by means of a mutual learning strategy, the KL divergence loss between the integrated soft distribution (z 1+z2)/2 of the soft distribution z 2 output by the first auxiliary classifier C1 and the soft distribution z 1 output by the second auxiliary classifier C2 and the soft distribution z f output by the fusion classifier is minimized, so that the classification knowledge of the inter-domain invariable representation learning network branches and the intra-domain invariable representation learning network branches is distilled into a network fusion module; the KL divergence loss between the fusion classifier output soft distribution z f and the first auxiliary classifier C1 output soft distribution z 1 and the second auxiliary classifier C2 output soft distribution z 2 is respectively minimized, so that knowledge of the network fusion module is distilled into an inter-domain invariable representation learning network branch and an intra-domain invariable representation learning network branch respectively; further, diversification of extracted features is facilitated by a feature difference maximization strategy that maximizes the Euclidean (L2) distance loss from inter-domain invariant features to intra-domain invariant features extracted from the labeled source domain.
S106-4: and setting fixed iteration training times, and stopping training when the model is stable.
The mutual learning strategy refers to that when the learning network branches are represented unchanged among training domains and the learning network branches and the network fusion module are represented unchanged in the domains, information is exchanged mutually through Kullback-Leibler (KL) divergence loss, so that the overall performance of the network is improved. The feature difference maximization strategy is to maximize the L2 distance between the inter-domain invariant features and the intra-domain invariant features, so that the inter-domain invariant representation learning network branches and the intra-domain invariant representation learning network branches extract different invariant features as much as possible, and the repetition and redundancy of the features are reduced, thereby improving the diversity of the features. The optimization algorithm includes, but is not limited to, one of a root mean square transfer algorithm, a random gradient descent algorithm, and an adaptive moment estimation algorithm.
Specifically, referring to fig. 3, the target domain test data set is input into the trained inter-domain invariable representation learning network branch, intra-domain invariable representation learning network branch and fusion classifier FC, and the health state type of the sample to be detected is identified online, namely, a fault type label is output.
Based on the above embodiment, in the present embodiment, taking the rolling bearing dataset provided by university of pamphlet KAt-DATACENTER of germany as an example, the dataset contains four health status data, respectively: the normal state (N), the inner ring fault (I), the outer ring fault (O) and the inner ring and outer ring composite fault (IO) are respectively represented by 0-3. The data under the four conditions employed in the experiment are described in table 1. The data under the same working condition form a domain, and the number of samples under different health states in each domain is 100. The auxiliary source domain consisting of one marked source domain and two unmarked domains in table 1 is used as training data set, and the remaining one domain is used as target domain test data set.
Table 1: experimental bearing dataset description
| Working conditions of | Rotational speed/(r/min) | Load torque/(Nm) | Radial force/(N) | Total number of samples |
| A | 1500 | 0.7 | 1000 | 400 |
| B | 900 | 0.7 | 1000 | 400 |
| C | 1500 | 0.1 | 1000 | 400 |
| D | 1500 | 0.7 | 400 | 400 |
Specifically, in the embodiment of the invention, the specific steps of the mechanical fault diagnosis method under the condition that the target working condition data is unavailable include:
S201: intercepting the time domain signal data sample to 4096 points in length, carrying out [0,1] normalization processing on the sample amplitude, dividing the data set into a multi-source domain data set and a target domain data set, and taking the preprocessed data as a model input sample.
S202: establishing inter-domain invariant representation learning network branches; the device comprises a first feature extractor F1, a first auxiliary classifier C1 and an auxiliary discriminator D, and is used for extracting inter-domain invariant features of input data samples;
the first feature extractor F1 consists of four one-dimensional convolution layers and four maximum pooling layers, wherein the rear of each one-dimensional convolution layer is connected with a ReLU activation function, and a preprocessed time domain signal data sample is taken as an input to output a high-level implicit feature vector with the length of 256;
The first auxiliary classifier C1 adopts a fully-connected network, two layers are designed in total, wherein the number of hidden layers is 512 and 4 respectively, the ReLU and Softmax activation functions are respectively connected after the two layers of fully-connected layers, the model finally outputs a four-dimensional vector to represent the health state type of input data, and the cross entropy of the marked source domain sample prediction fault type label is calculated through the label output probability of the first auxiliary classifier C1;
The auxiliary discriminator D adopts a fully-connected network, two layers are designed, wherein the dimension of an implicit layer is 512 and 1 respectively, the two layers of fully-connected layers are connected with a ReLU and a Sigmoid activation function respectively, the model finally outputs a one-dimensional vector to represent the domain class of input data, and the binary cross entropy of the multi-source domain sample prediction domain class label is calculated through the label output probability of the auxiliary discriminator D.
S203: establishing intra-domain invariant representation learning network branches; is composed of a Fast Fourier Transform (FFT) module, a second feature extractor F2 and a second auxiliary classifier C2 for intra-domain invariant feature extraction of input data samples.
The second feature extractor F2 consists of four one-dimensional convolution layers and four maximum pooling layers, wherein the rear of each one-dimensional convolution layer is connected with a ReLU activation function, and a preprocessed time domain signal data sample is taken as an input to output a high-level implicit feature vector with the length of 256;
The second auxiliary classifier C2 adopts a fully-connected network, two layers are designed, wherein the number of hidden layers is 512 and 4 respectively, the ReLU and Softmax activation functions are respectively connected after the two layers of fully-connected layers, the model finally outputs a four-dimensional vector to represent the health state type of input data, and the cross entropy of the marked source domain sample prediction fault type label is calculated through the label output probability of the auxiliary classifier C2;
S204: establishing a fusion classifier FC; the method is used for fusing the inter-domain invariant features and the intra-domain invariant features and predicting fault class labels of the fused features.
The fusion classifier FC adopts a fully-connected network, two layers are designed, wherein the dimension of an implicit layer is 512 and 4 respectively, the two layers of fully-connected layers are respectively connected with a ReLU and a Softmax activation function, and finally, a four-dimensional vector is output by a model to represent the health state type of input data; and calculating the cross entropy of the labeled source domain sample prediction fault class label by fusing the label output probability of the classifier FC.
S205: constructing a fault diagnosis training model; and combining the inter-domain invariable representation learning network branches, the intra-domain invariable representation learning network branches and the fusion classifier FC to construct a complete fault diagnosis training model.
S206: the multi-source domain data set is input into a constructed fault diagnosis training model, the overall performance of the model is improved through a mutual learning strategy, the diversity of extracted features is improved through a feature difference maximization strategy, and model training is carried out according to a given loss function and an optimization algorithm.
The objective loss function of the fault diagnosis training model comprises optimized training objective losses of the first feature extractor F1, the first auxiliary classifier C1, the auxiliary discriminator D, the second feature extractor F2, the second auxiliary classifier C2 and the fusion classifier C, inter-domain invariance represents learning network branches and intra-domain invariance represents mutual learning losses of the learning network branches and the network fusion module, and inter-domain invariance features are distance losses of intra-domain invariance features.
The optimization algorithm adopts a self-adaptive moment estimation algorithm, the learning rate is 0.001, the model objective function loss tends to be stable after 200 iterations, and the model training is finished.
S207: and inputting the target domain test data set into the trained inter-domain invariable representation learning network branch, intra-domain invariable representation learning network branch and fusion classifier FC, and identifying the health state type of the sample to be detected on line.
Referring to fig. 4, a visualization of extraction features of rolling bearing data samples in different domains by t-SNE technology is shown, wherein a source domain a with a fault type label is used as a marked source domain, source domains C and D without a type label are combined to form an unmarked auxiliary source domain, and data under a working condition B is used as a target domain. As can be seen from fig. 4, the features of the same category of the three source domains and the target domains participating in training can be effectively aligned, the boundaries between the features of different categories are obvious, and the method has strong distinguishability, so that the method can obtain cross-domain migration knowledge, learn the representation irrelevant to distribution, learn the feature representation more useful to classification, and extract the inter-domain invariant features and intra-domain invariant features.
Referring to fig. 5, the confusion matrix of the target domain test data set diagnosis result by the method of the present invention shows that the accuracy of the diagnosis of the target domain data in three health states by the method reaches 100%, and the accuracy of the identification of the residual inner ring fault (I, labeled 1) target data reaches 86%, which indicates that the method has excellent performance in the aspect of fault diagnosis under unknown target working conditions.
In summary, by establishing the inter-domain invariant representation learning network branches and the intra-domain invariant representation learning network branches, sufficient cross-domain migration knowledge can be learned, more domain invariant features which are more useful in classification can be obtained, the overall performance of the model can be improved through mutual learning strategies and feature difference maximization strategies, more diversified features are extracted, the generalization capability of the model is remarkably improved, and accurate diagnosis of mechanical faults under target working conditions which do not participate in model training is realized.
The embodiment of the invention also provides a mechanical fault diagnosis device under the condition that the target working condition data is unavailable, comprising:
The data preprocessing module 100 is used for intercepting the acquired mechanical vibration time domain signal, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
An inter-domain invariant representation learning network branch construction module 200 for constructing an inter-domain invariant representation learning network branch based on the first feature extractor, the first auxiliary classifier and the auxiliary discriminator, for extracting inter-domain invariant features of the input sample;
The intra-domain invariant representation learning network branch construction module 300 is configured to construct an intra-domain invariant representation learning network branch based on the fast fourier transform module, the second feature extractor and the second auxiliary classifier, and to extract intra-domain invariant features of the input sample;
The fusion classifier construction module 400 is configured to construct a fusion classifier based on a full-connection layer and a Softmax classifier, and is configured to fuse the inter-domain invariant feature and the intra-domain invariant feature, and predict a fault class label of the fused feature;
the fault diagnosis training model construction module 500 is configured to construct a fault diagnosis training model including the inter-domain invariant representation learning network branch, the intra-domain invariant representation learning network branch, and the fusion classifier;
The fault diagnosis training model training module 600 is configured to input the samples in the multi-source domain data set into the fault diagnosis training model, perform model training by using a mutual learning strategy, a feature difference maximizing strategy, a loss function and an optimization algorithm, and obtain a trained fault diagnosis training model;
The fault diagnosis module 700 is configured to input the samples in the target domain data set into a trained fault diagnosis training model, and obtain fault types of the samples.
The mechanical fault diagnosis device with unavailable target operating condition data in this embodiment is used to implement the mechanical fault diagnosis method with unavailable target operating condition data, so that the specific implementation of the mechanical fault diagnosis device with unavailable target operating condition data may refer to the embodiment parts of the mechanical fault diagnosis method with unavailable target operating condition data in the foregoing, for example, the data preprocessing module 100, the inter-domain invariant representation learning network branch construction module 200, the intra-domain invariant representation learning network branch construction module 200, the fusion classifier construction module 400, the fault diagnosis training model construction module 500, the fault diagnosis training model training module 600, and the fault diagnosis module 700 respectively implement steps S101, S102, S103, S104, S105, S106 and S107 in the mechanical fault diagnosis method with unavailable target operating condition data.
The embodiment of the invention also provides a mechanical fault diagnosis device under the condition that the target working condition data is unavailable, comprising:
a memory for storing a computer program;
And the processor is used for realizing the steps of the mechanical fault diagnosis method under the condition that the target working condition data is unavailable when the computer program is executed.
According to the mechanical fault diagnosis method under the condition that target working condition data are unavailable, inter-domain invariant representation learning network branches and intra-domain invariant representation learning network branches are utilized to extract inter-domain invariant features and intra-domain invariant features of sample data, and a fusion classifier is utilized to fuse the inter-domain invariant features and the intra-domain invariant features, so that mobility and class discrimination of extracted features are improved; in the model training stage, the inter-domain invariable representation learning network branches and the intra-domain invariable representation learning network branches and the fusion classifier are mutually learned by utilizing a mutual learning strategy, so that the improvement of the overall performance of the model is promoted, and the fault diagnosis accuracy is improved; by using a feature difference maximization strategy, the repetition and redundancy of extracted features are reduced by maximizing Euclidean distance between inter-domain invariant features and intra-domain invariant features, feature diversification is promoted, the generalization capability on unavailable target working condition data is strong, and the application range is wide; the generalization capability of the model is remarkably improved, and the accurate diagnosis of mechanical faults under the target working condition without participating in model training is realized.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. A method for diagnosing a mechanical failure in the event that target operating condition data is unavailable, comprising:
S1, intercepting an acquired mechanical vibration time domain signal, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
s2, constructing an inter-domain invariable representation learning network branch based on the first feature extractor, the first auxiliary classifier and the auxiliary discriminator, and extracting inter-domain invariable features of an input sample;
s3, constructing a domain invariant representation learning network branch based on the fast Fourier transform module, the second feature extractor and the second auxiliary classifier, wherein the domain invariant representation learning network branch is used for extracting domain invariant features of an input sample;
S4, constructing a fusion classifier based on the full connection layer and the Softmax classifier, and fusing the inter-domain invariant features and the intra-domain invariant features and predicting fault class labels of the fused features;
S5, constructing a fault diagnosis training model comprising the inter-domain invariable representation learning network branch, the intra-domain invariable representation learning network branch and the fusion classifier;
S6, inputting samples in the multi-source domain data set into the fault diagnosis training model, and performing model training by using a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model;
s7, inputting the samples in the target domain data set into a trained fault diagnosis training model, and obtaining fault types of the samples.
2. The method for diagnosing mechanical faults under the condition that target working condition data are unavailable according to claim 1, wherein the data set is divided according to different working conditions of the machine, the same rotating speed and load are the same working condition, multiple health state samples under the same working condition are placed in the same domain, and the types of the health states of the machine contained in different domains are the same; the multi-source domain dataset comprises a marked source domain and a plurality of unmarked auxiliary source domains, which are used for training a model; the target domain dataset is not available during the model training phase and does not participate in model training.
3. The method for diagnosing mechanical failure under condition data of no availability according to claim 2, wherein in step S6, the training process of the failure diagnosis training model includes:
In the inter-domain invariant representation learning network branch, minimizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the auxiliary discriminant D, maximizing the binary cross entropy loss L adv of the auxiliary discriminant D to optimize the first feature extractor F1 for sample data from the labeled source domain and the unlabeled auxiliary source domain; minimizing the cross entropy loss L s1 of the first auxiliary classifier C1 to optimize the first auxiliary classifier C1 and the first feature extractor F1 for the labeled sample data from the source domain;
Performing a fast fourier transform on the labeled sample data from the source domain in the domain invariant representation learning network branch to minimize the cross entropy penalty L s2 of the second auxiliary classifier C2 to optimize the second auxiliary classifier C2 and the second feature extractor F2;
in the fusion classifier FC, minimizing cross entropy loss L s3 of the fusion classifier FC for labeled sample data from the multi-source domain dataset to optimize the fusion classifier FC, the first feature extractor F1, and the second feature extractor F2; the KL divergence loss between the integrated soft distribution (z 1+z2)/2 of the soft distribution z 2 output by the first auxiliary classifier C1 and the soft distribution z 1 output by the second auxiliary classifier C2 and the soft distribution z f output by the fusion classifier FC is minimized through a mutual learning strategy, and classification knowledge of the inter-domain invariable representation learning network branches and the intra-domain invariable representation learning network branches is distilled into the fusion classifier; respectively minimizing KL divergence loss between the fusion classifier output soft distribution z f and the first auxiliary classifier C1 output soft distribution z 1 and the second auxiliary classifier C2 output soft distribution z 2, and respectively distilling knowledge of the fusion classifier into an inter-domain invariable representation learning network branch and an intra-domain invariable representation learning network branch; maximizing Euclidean distance loss from inter-domain invariant features extracted from the labeled source domain to intra-domain invariant features through a feature difference maximization strategy; and setting fixed iterative training times, stopping training until the fault diagnosis training model is stable, and outputting a trained fault diagnosis training model.
4. The method for diagnosing a mechanical failure under conditions where target operating condition data is not available according to claim 2, wherein in step S2, extracting an inter-domain invariant feature of the input sample includes:
the first feature extractor F1 takes a marked source domain and an unmarked auxiliary source domain as inputs and outputs high-level implicit features;
The first auxiliary classifier C1 and the auxiliary discriminator D take the high-level implicit features extracted by the first feature extractor F1 as inputs, and output fault class labels and domain class labels respectively, wherein the high-level implicit features extracted by the first feature extractor F1 are inter-domain invariant features.
5. The method for diagnosing a mechanical failure under conditions where target operating condition data is not available according to claim 2, wherein in step S3, extracting the domain invariant feature of the input sample includes:
The fast Fourier transform module takes a mechanical vibration time domain signal in a marked source domain as input, converts the mechanical vibration time domain signal into a mechanical vibration frequency domain signal, analyzes the frequency spectrum of the mechanical vibration frequency domain signal, and acquires frequency domain characteristics;
The second feature extractor F2 takes the mechanical vibration frequency domain signal as input and outputs high-level implicit features;
the second auxiliary classifier C2 takes the high-level hidden feature extracted by the second feature extractor F2 as input, outputs a fault class label, and the high-level hidden feature extracted by the second feature extractor F2 is an intra-domain invariant feature.
6. The method of claim 1, wherein the first feature extractor and the second feature extractor are each constructed using one of, but not limited to, a fully connected network, a deep convolution network, a deep belief network, and a deep residual network;
the first auxiliary classifier and the second auxiliary classifier both comprise a full connection layer and a Softmax classifier, and the label classification loss of the Softmax classifier is the cross entropy loss of the sample prediction class label in the labeled source domain;
The auxiliary discriminator comprises a full-connection layer and a Sigmoid activation function, and the domain discrimination loss of the auxiliary discriminator is a binary cross entropy loss of a sample prediction domain label in the multi-source domain data set.
7. The method according to claim 1, wherein in step S4, the inter-domain invariant feature and the intra-domain invariant feature are fused by adding corresponding elements in a feature vector.
8. The method for diagnosing a mechanical failure under the condition that target working condition data is not available according to claim 1, wherein in step S6, the mutual learning strategy is to use KL divergence loss Kullback-Leibler to exchange information with each other when training the inter-domain invariable representation learning network branch, the intra-domain invariable representation learning network branch and the fusion classifier; the feature difference maximization strategy reduces the repetition and redundancy of features by maximizing the Euclidean distance between the inter-domain invariant features and the intra-domain invariant features; the optimization algorithm includes, but is not limited to, one of a root mean square transfer algorithm, a random gradient descent algorithm, and an adaptive moment estimation algorithm.
9. A mechanical failure diagnosis device under which target operating condition data is not available, characterized by comprising:
the data preprocessing module is used for intercepting the acquired mechanical vibration time domain signals, unifying the sample length and carrying out amplitude normalization to obtain a data set; dividing the data set into a multi-source domain data set and a target domain data set;
The inter-domain invariable representation learning network branch construction module is used for constructing inter-domain invariable representation learning network branches based on the first feature extractor, the first auxiliary classifier and the auxiliary discriminator and extracting inter-domain invariable features of input samples;
The intra-domain invariant representation learning network branch construction module is used for constructing intra-domain invariant representation learning network branches based on the fast Fourier transform module, the second feature extractor and the second auxiliary classifier and extracting intra-domain invariant features of the input samples;
the fusion classifier construction module is used for constructing a fusion classifier based on the full connection layer and the Softmax classifier, fusing the inter-domain invariant features and the intra-domain invariant features, and predicting fault class labels of the fused features;
The fault diagnosis training model construction module is used for constructing a fault diagnosis training model comprising the inter-domain invariable representation learning network branches, the intra-domain invariable representation learning network branches and the fusion classifier;
The fault diagnosis training model training module is used for inputting samples in the multi-source domain data set into the fault diagnosis training model, and performing model training by utilizing a mutual learning strategy, a characteristic difference maximization strategy, a loss function and an optimization algorithm to obtain a trained fault diagnosis training model;
and the fault diagnosis module is used for inputting the samples in the target domain data set into a trained fault diagnosis training model to obtain fault categories of the samples.
10. A mechanical failure diagnosis apparatus for a situation in which target operating condition data is not available, comprising:
a memory for storing a computer program;
A processor for implementing the steps of a method for diagnosing mechanical failure in the event that target operating condition data is not available as claimed in any one of claims 1 to 8 when executing said computer program.
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| Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions;Yifei Ding等;Knowledge-Based Systems;20221215;全文 * |
| 基于物联网的矿山机械设备状态智能感知与诊断;丁恩杰;俞啸;廖玉波;吴传龙;陈伟;郁万里;王威;;煤炭学报;20200910(第06期);全文 * |
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