CN117574259B - Attention twin intelligent migration interpretability diagnosis method suitable for high-end equipment - Google Patents
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Abstract
The invention discloses an attention twin intelligent migration interpretability diagnosis method suitable for high-end equipment, which comprises the steps of firstly obtaining vibration signal data of different fault parts of a bearing and a gear under different loads or rotating speeds, and preprocessing the data; constructing a convolution neural network model of a double-branch architecture, so that the model can simultaneously receive unbalanced samples under two different working conditions for training; adding a cross-module serial dual-attention mechanism comprising a channel feature attention enhancement module and a segment feature attention enhancement module into the model, wherein the cross-module serial dual-attention mechanism is used for reasonably enhancing the feature information of small samples, enhancing the domain invariant features of unbalanced samples and realizing the attention interpretability of the features; meanwhile, a twin feature fusion module is introduced, so that domain invariant features in an unbalanced sample are better extracted; finally, various proposed feature visualization methods are adopted to realize feature visualization interpretation of the migration diagnosis model, and high-efficiency migration diagnosis accuracy is completed on the test data set.
Description
Technical Field
The invention relates to an intelligent diagnosis technology of high-end equipment, in particular to an attention twin intelligent migration interpretability diagnosis method suitable for high-end equipment.
Background
The rotating parts such as the bearing or the gear are core parts for power transmission of high-end equipment, and the health state of the rotating parts directly influences the operation reliability of the high-end equipment, so that the fault monitoring and diagnosis of the rotating parts such as the bearing or the gear are very important. At present, fault monitoring and diagnosing methods are mainly divided into three categories: A method based on a kinetic model; /(I) A method of extracting a mechanism feature based on signal processing; /(I)An intelligent fault diagnosis method based on artificial intelligence. The method based on the dynamic model is the basis of fault diagnosis, but because the method is difficult to simulate the fault dynamic characteristics of real high-end equipment in operation, researchers gradually start to study a signal processing method capable of extracting fault mechanism characteristics, and the running state of the rotating part can be judged according to the extracted corresponding mechanism characteristics. In recent decades, intelligent fault diagnosis methods have been increasingly applied to the field of fault diagnosis due to the rise of artificial intelligence technology. In the intelligent fault diagnosis application process, the problem of migration working conditions caused by rotation speed or load change is frequently encountered; sample imbalance problems caused by more normal samples and fewer failed samples are also frequently encountered; for high-end equipment, such as geared turbofan aircraft engines, problems are also often encountered in which both bearings and gears need to be diagnosed.
In the intelligent diagnosis engineering application, the problem of migration working conditions often causes drift of fault characteristics, so various migration learning methods are often used for reasonably measuring and limiting the drift of characteristics, such as a migration measurement method mentioned in patent CN 113435375B. The problem of sample unbalance often causes excessive misclassification of a small sample by a model and also causes simultaneous occurrence of migration problems, and a method for fault migration diagnosis of a rolling bearing with multitasking and freedom of the unbalanced sample is proposed in the patent CN 113469066B, which solves the problem.
However, aiming at the synchronous diagnosis problem of the migration working conditions of the bearing and the gear under the unbalanced sample of the high-end equipment like the gear-driven turbofan aeroengine, the corresponding patents are fewer; and also lacks various feature visualization methods for such cases. For example: the CN113191215A uses two-dimensional time-frequency data, has low processing speed, has good effect only under the condition of unbalanced sample, and has general diagnosis on the migration of working conditions; patent CN113505655A cannot solve the problem of simultaneous diagnosis under variable rotation speed or variable load migration. In summary, the existing intelligent diagnosis method is difficult to realize synchronous feature interpretable fault diagnosis of the migration working condition of the bearing and the gear under the unbalanced sample.
Disclosure of Invention
The invention aims to: the invention aims to solve the defects in the prior art, provides a method for diagnosing the intelligent migration interpretability of the attention twin suitable for high-end equipment, can diagnose migration working conditions of a bearing and a gear under unbalanced samples at the same time, adopts various feature visualization methods based on the attention or twin networks to visually interpret the features learned by an intelligent diagnosis model, and promotes the practical application process of intelligent fault diagnosis.
The technical scheme is as follows: the invention discloses an attention twin intelligent migration interpretability diagnosis method suitable for high-end equipment, which comprises the following steps of:
Step S1, obtaining fault signals of a bearing and a gear in a high-end equipment (such as a gear drive turbofan aeroengine) simulation test bed to obtain a bearing fault sample and a gear fault sample;
step S2, carrying out data preprocessing on a bearing fault sample and a gear fault sample: sequentially combining three aero-bearing data under variable load and three planetary gear data under variable rotation speed, adding healthy samples (namely normal sample data), setting each type of healthy samples into different unbalanced sample sizes, performing time-frequency conversion on the unbalanced samples into frequency domain signals (for example, short-time Fourier conversion can be adopted), and performing standardized processing;
s3, constructing an attention twin intelligent migration interpretable diagnostic model
Step S3.1, building a double-branch convolutional neural network model, wherein the double-branch convolutional neural network model is divided into 5 convolutional layers, 5 pooling layers and a full-connection layer, and the model comprises a batch standardization layer and L2 regularization;
S3.2, designing a cross-module double-attention mechanism module which is connected in series with the cross-module for better diagnosis effect and better interpretability of the intelligent diagnosis model, and establishing a feature visualization method of the corresponding module;
The cross-module dual-attention mechanism module comprises a channel feature attention module CFAE and a segment feature attention module FFAE, wherein the channel feature attention module CFAE is used for enhancing the feature values in different channels of the unbalanced sample, and the segment feature attention module FFAE is used for enhancing the feature values of different feature segments of the unbalanced sample;
Step S3.3, in order to better extract the characteristic information in the unbalanced sample, and simultaneously extract as many fusion domain invariant features under different loads or rotating speeds as possible for the model, and introduce a contrast loss function to construct a twin feature fusion network module;
s3.4, inserting a cross-module double-attention mechanism module and a twin feature fusion network module into the double-branch convolutional neural network model to construct an attention twin intelligent migration interpretable diagnostic model under an unbalanced sample; wherein after the channel feature attention module CFAE is placed at the 3 rd pooling layer, the segment feature attention module FFAE is placed at the 5 th convolution layer, applying a twin feature fusion network at the Global Average Pooling (GAP) layer;
Step S4, training an attention twin intelligent migration interpretable diagnostic model by using unbalanced sample data standardized in the step S2, wherein the attention twin intelligent migration interpretable diagnostic model is trained by using unbalanced sample data under any two loads or rotating speeds, the unbalanced sample data under two working conditions are used for training at first, and then data under other working conditions are used for testing (the limitation that the traditional migration diagnostic method needs to use target domain data of the same class as the source domain sample for follow-up training is avoided), so that the practical fault diagnosis application requirements (the target domain data which is of the same class as the source domain data and the quantity of which is as consistent as possible is difficult to obtain in the practical application of migration diagnosis) are more satisfied;
S5, testing the diagnostic effect of the attention twin intelligent migration explanatory diagnostic model by using a test set; parameters such as internal weight and bias of the trained attention twin intelligent migration interpretable diagnostic model are fixed, further, diagnostic effects of the model are verified by using data under other rotating speeds or loads, a diagnostic comparison box graph and a confusion matrix with other models are drawn, and model performance is displayed;
Step S6, visually displaying the statistical features learned by the attention twin intelligent migration interpretable diagnostic model by means of the cross-module double-attention mechanism module in the step S3, firstly displaying the enhancement of the channel features by the channel feature attention module CFAE, then displaying the enhancement of the features of different fragments by the fragment feature attention module FFAE, and simultaneously visually displaying the activation weight W2, so that the statistical feature information is specifically learned by the display model by combining with the spectrum waveform;
and S7, displaying cross-domain fusion and invariant features which are not changed along with working conditions in the unbalanced sample under the working conditions learned by the attention twin intelligent migration interpretability diagnostic model by means of the step S3 twin feature fusion network module, and further explaining feature information learned by the intelligent model.
Further, the dual-branch convolutional neural network model in the step S3.1 includes 5 convolutional layers, 5 pooling layers and a full-connection layer, and further includes a batch normalization layer and L2 regularization, and the formulas thereof are as follows:
Where m represents the total amount of samples per batch, Sample representing each batch,/>And/>Represents the average and standard deviation, respectively,/>, of the batch dataAnd/>Respectively represent corrected sample values,/>For avoiding the phenomenon that the denominator standard deviation is 0, gamma and beta are scale factors and translation factors respectively;
where E in is the original classification loss function, Is a regularization parameter, w represents a weight parameter in the model.
Further, the details of the cross-module dual-attention mechanism module in step S3.2 are as follows: the expression of the channel feature attention module CFAE is:
where CFAE is the characteristic output of the CFAE module, z (Pool 3) represents the characteristic of the third pooled layer output, z activate represents the characteristic of attention activation, Is a correction coefficient, C represents the number of fault categories, and e is a constant;
The expression of the segment feature attention module FFAE is:
Where ffae is the signature output of the FFAE module, W2 represents the activation weights for the different fragments of each sample, z j,m (i) is the signature output of the layer above FFAE, and a 1~A8 is the sample activation value for each signature fragment.
Further, the expression of the loss function in the step S3.3 is:
In the method, in the process of the invention, The parameters representing the contrast loss layer, L Siamese is the loss of the twinned signature fusion network, D (G 1,G2) represents the distance between unbalanced sample signatures from two speeds or loads (G 1,G2), Q is a constant, L represents the network layer, N represents the number, and N refers to the total number of samples.
Further, the total loss function of the attention twin intelligent migration interpretable diagnostic model obtained in step S3.4 may be expressed as:
where N represents the number, C represents the number of failure categories, y is the sample label, p is the probability that the label is correct, L sum represents the total loss, Parameter representing cross entropy loss,/>Parameters representing regularization loss, L entropy is the classification loss cross entropy loss term, L L is the regularization loss term, and N represents the total number of samples.
Further, the enhanced values of the channel feature attention module CFAE for different channel features in step S7 are as follows:
Where z activate represents the value after feature attention activation, W1 is the attention weight,/> Is a correction factor, z (pool_3) represents the characteristic of the output of the third pooled layer, z activate represents the characteristic of attention activation,/>Is a correction coefficient, C represents the number of fault categories;
the value of the segment feature attention module FFAE weight is formulated as follows:
where W2 represents the activation weights for different segments of each sample, z j,m (i) is the feature output of the layer above FFAE, and A 1~A8 is the sample activation value for each feature segment.
The beneficial effects are that: the invention provides an intelligent diagnosis method for variable rotation speed or variable load migration under an unbalanced sample for different types of faults of a bearing and a gear, and the method effectively enhances the characteristic information of a small sample under the unbalanced sample through a cross-module serial dual-attention mechanism designed by the invention, so that the model can better extract the characteristics in the unbalanced sample; meanwhile, under the constraint of the proposed twin feature fusion network, the model extracts domain invariant features under an unbalanced sample which does not change along with working conditions, so that data under other working conditions can be better diagnosed; finally, the information learned by the model is visually interpreted through the attention mechanism and the characteristics in the twin characteristic fusion network respectively, so that the internal interpretability of the intelligent diagnosis model is enhanced.
Drawings
FIG. 1 is an overall flow chart of the intelligent diagnosis of the present invention;
FIG. 2 is a schematic diagram of an illustrative model of attention twin intelligent migration under an unbalanced sample of the present invention;
FIG. 3 is a graph showing the diagnostic performance of the model of the present invention under different migration conditions;
FIG. 4 is a confusion matrix of the model in the working condition of D1/D2-D3 in the invention;
FIG. 5 is a graph of the enhancement effect of the CFAE module on different channel characteristics in the present invention;
FIG. 6 is a diagram showing the enhancement effect of FFAE module on different segment characteristics according to the present invention;
fig. 7 is a plot of attention versus frequency domain feature matching.
FIG. 8 is a graph of the unchanged feature of the unbalanced sample domain extracted by the twinning feature fusion network layer in the present invention.
Detailed Description
The technical scheme of the present invention is described in detail below, but the scope of the present invention is not limited to the embodiments.
As shown in fig. 1 and fig. 2, the attention twin intelligent migration interpretability diagnosis method suitable for high-end equipment can diagnose a bearing and a gear at the same time, and adopts various attention or twin network-based feature visualization methods to carry out visual interpretation on the features learned by an intelligent diagnosis model so as to promote the practical application process of intelligent fault diagnosis. The method specifically comprises the following steps:
Step S1: and acquiring fault signals of the bearing and the gear in the simulation test bed of the high-end equipment of the gear drive turbofan aeroengine, and further acquiring a bearing fault sample and a gear fault sample.
The embodiment adopts the data of variable load (1000N, 1400N and 1800N) of the ultra-high-speed aviation bearing of the university of Duling engineering at 18000r/min, and the model is verified for simultaneously diagnosing the bearing and the planet gear by adopting the data of the self-contained planet gear at variable rotation speed (1800 r/min,2000r/min and 2500 r/min) and combining the two data because the aviation planet gear is very expensive.
Step S2: data preprocessing of bearing failure samples and gear failure samples
Sequentially combining three aviation bearing data under variable load and three planetary gear data under variable rotation speed, adding health samples, setting each type of health samples into different unbalanced sample sizes, performing time-frequency conversion on the health samples to convert the health samples into frequency domain signals, and performing standardized processing.
The present embodiment is divided into seven health conditions: normal samples (NR), bearing inner ring indentations 150 μm (IRI 150), bearing inner ring indentations 450 μm (IRI 450), bearing roller indentations 450 μm (RI 450), planet wheel pitting (PGP), sun wheel pitting (SGP) and sun wheel wear (SGW); for the seven types of health conditions, different numbers of sample sizes are respectively set, namely, the minimum number of samples is 100, and the maximum number of samples is 500. As shown in fig. 1, in this embodiment, the unbalanced sample, the working condition 1 and the working condition 2 may form two training sets, and the balanced sample and the working condition 3 may form a test set; the first two training sets input an untrained diagnosis model at the same time, and after the model is trained, the data set under the third working condition is used for verification.
Step S3: construction of attention twin intelligent migration interpretable diagnostic model
Step S3.1: building a double-branch convolutional neural network model, wherein the double-branch convolutional neural network model is divided into 5 convolutional layers, 5 pooling layers and a full-connection layer, the model comprises a batch normalization layer and L2 regularization, and the formulas are as follows:
In the method, in the process of the invention, And/>Represents the average and standard deviation, respectively,/>, of the batch dataFor avoiding the phenomenon that the denominator standard deviation is 0, gamma and beta are scale factors and translation factors respectively;
where E in is the original classification loss function, Is a regularization parameter, w represents a weight parameter in the model;
step S3.2: in order to make the diagnosis effect of the intelligent diagnosis model better and more interpretable, a cross-module double-attention mechanism module is designed, and a feature visualization method of a corresponding module is established.
The cross-module dual-attention mechanism module of the present embodiment includes a channel feature attention module (CFAE) and a segment feature attention module (FFAE), where the channel feature attention module (CFAE) enhances feature values in different channels of an unbalanced sample by attention, and the total formulas after attention activation and after residual linking are expressed as:
Where z activate represents the value after feature attention activation, W1 is the attention weight, Is a correction coefficient, CFAE is the characteristic output of the CFAE module, z (pool_3) represents the characteristic of the output of the third pooled layer, z activate represents the characteristic of attention activation,/>Is a correction coefficient, C represents a category;
The segment feature attention module (FFAE) enhances the feature values of different feature segments of the unbalanced sample by attention, and the attention weight W2 and the attention formula are expressed as:
Where ffae is the characteristic output of the FFAE module, W2 represents the activation weight for each sample for a different segment, z j,m (i) is the characteristic output of the layer above FFAE, and a 1~A8 is the sample for each characteristic segment activation value;
Step S3.3: in order to better extract the characteristic information in the unbalanced sample, and simultaneously extract as many fusion domain invariant features under different loads or rotating speeds as possible for the model, a contrast loss function is introduced to construct a twin feature fusion network module, and the loss function and a distance formula of the twin feature fusion network module are respectively expressed as follows:
In the method, in the process of the invention, The parameters representing the contrast loss layer, L Siamese is the loss of the twinned feature fusion network, D (G 1,G2) represents the distance between unbalanced sample features from two speeds or loads (G 1,G2), Q is a constant, L represents the network layer, n represents the number, dim represents the dimension;
Step S3.4: inserting the dual-attention mechanism module and the twin feature fusion network module into a dual-branch convolution network model to construct an attention twin intelligent migration interpretable diagnostic model under an unbalanced sample;
in the attention twin intelligent migration interpretable diagnostic model, after the channel feature attention module CFAE is placed at the third pooling layer, the segment feature attention module FFAE is placed at the 5 th convolution layer, applying a twin feature fusion network at the Global Average Pooling (GAP) layer;
the total loss function of the attention twin intelligent migration interpretable diagnostic model is expressed as:
where N represents the number, C represents the number of failure categories, y is the sample label, p is the probability that the label is correct, L sum represents the total loss, Parameter representing cross entropy loss,/>The parameters representing regularization loss, L entropy, are classification loss cross entropy loss terms, and L L are regularization loss terms.
The detailed parameters of the attention twin smart migration interpretable diagnostic model of the present embodiment are shown in Table 1:
Table 1 model detailed parameter table
| Layer number | Layer type | Core size/step size | Number of channels | Batch normalization | Activation function |
| 1 | Conv_1 | 45*1/3*1 | 16 | Yes | Relu |
| 2 | Pool_1 | 2*1/2*1 | 16 | / | / |
| 3 | Conv_2 | 15*1/3*1 | 32 | Yes | Relu |
| 4 | Pool_2 | 2*1/2*1 | 32 | / | / |
| 5 | Conv_3 | 3*1/1*1 | 48 | Yes | Relu |
| 6 | Pool_3 | 2*1/2*1 | 48 | / | / |
| 7 | CFAE | / | 48 | / | / |
| 8 | Conv_4 | 3*1/1*1 | 64 | Yes | Relu |
| 9 | Pool_4 | 2*1/2*1 | 64 | / | / |
| 10 | Conv_5 | 3*1/1*1 | 64 | Yes | Relu |
| 11 | FFAE | / | 64 | / | |
| 12 | GAP | / | 64 | / | / |
| 13 | SFF | / | / | / | / |
| 14 | Fc1 | 64*10 | / | / | / |
| 15 | / | / | / | / | Softmax |
Step S4: training the attention twin intelligent migration interpretive diagnostic model using the unbalanced sample data (two training sets in fig. 1) prepared in step S2, wherein the attention twin intelligent migration interpretive diagnostic model is trained by using unbalanced sample data under any two loads or rotating speeds, namely: firstly training by using unbalanced sample data under two working conditions, and then testing by using data under other working conditions;
Step S5: performing model diagnosis effect test on the attention twin intelligent migration interpretable diagnosis model;
The parameters such as the internal weight and bias of the attention twin intelligent migration interpretable diagnostic model trained in the step S4 are fixed, further, the diagnostic effect of the model is verified by using data under other rotating speeds or loads, a diagnostic performance of the model under different migration working conditions and a confusion matrix of the model of FIG. 4 under the working conditions of D1/D2-D3 are drawn, and the performance of the model is displayed.
Step S6: and visually displaying the statistical features learned by the attention twin intelligent migration explanatory diagnosis model by means of the cross-module double-attention mechanism module.
Firstly, enhancing the characteristics of different channels by the CFAE module, and drawing an effect diagram of enhancing the characteristics of different channels by the CFAE module of FIG. 5; then, the display FFAE module carries out feature enhancement on different fragments, and simultaneously carries out visual display on the activation weight W2, and the effect diagram of feature enhancement on different fragments is drawn by the module of fig. 6 FFAE; and further, combining the spectrum waveform, showing which statistical characteristic information is specifically learned by the model, and drawing a attention and frequency domain characteristic matching diagram of fig. 7.
Step S7: and displaying the cross-domain fusion and invariant features which are not changed along with the working condition in the unbalanced sample under the variable working condition learned by the attention twin intelligent migration interpretability diagnostic model by means of the twin feature fusion network module, drawing an unbalanced sample domain invariant feature map extracted by the twin feature fusion network layer of FIG. 8, and further explaining the feature information learned by the intelligent model.
Examples:
The feasibility of the present invention was verified by taking as an example the intelligent fault diagnosis of bearings and gears in the geared turbofan aeroengine simulation test stand described in steps 1 and 2 of the above-described embodiments (see fig. 2).
The imbalance samples under each condition of this example were prepared as shown in table 2:
TABLE 2 sample size for bearing and gear imbalance samples under different conditions
In Table 2, D1 to D3 represent migration conditions 1 to 3, respectively. It can be seen from the table that the number of samples of each type is ragged, with a minimum of 100 samples and a maximum of 500 samples. The sample data are used, unbalanced sample data under two working conditions are used for training, and then data under a third working condition are used for testing the model, and the diagnosis performance of the model under different working conditions is shown in figure 3. When the abscissa of fig. 3 represents each operating condition, the dataset symbol "D" is omitted for convenience. In FIG. 3, 1/2.fwdarw.3 represents the working condition of D1/D2.fwdarw.D3, and so on. It can be seen from the figure that the proposed model achieves a rather good diagnostic effect. Even under such severe conditions, uneven unbalanced samples are used as training, and the diagnosis accuracy is higher than 96% for a plurality of detection components, and the diagnosis stability is good. However, for D1/D3- > D2 and D3/D2- > D1, the test accuracy of the model is even slightly higher than the training accuracy, which also proves the adaptive robustness of the model to some extent.
In addition, as can be seen from the confusion matrix of fig. 4, the attention twin intelligent migration interpretable diagnostic model of the present invention correctly classifies these confusing unbalanced samples, further demonstrating the advancement of the present invention.
As can be seen from fig. 5, the CFAE module enhances the characteristics of the CFAE layer relative to the characteristics of the upper layer (pool_3). This enhancement occurs whether the absolute value is large or small. The feature increase amplitude with smaller absolute value is relatively smaller, and the feature increase amplitude with larger absolute value is relatively larger. Since the principle of CFAE is to enhance the eigenvalues of different channels in the model, each channel (channel 1, channel 13, channel 25, channel 37) has a certain effect, it is placed in front of the segment feature enhancement network. To apply the performance of the CFAE network to the more specific features extracted by the model as early as possible, it is placed after the third pooling layer.
As can be seen from the segment feature enhancement effect of the model FFAE in fig. 6, FFAE enhances the segment with a larger absolute value of the selected feature, ignores the part with a smaller feature value, and makes the model have a better classification effect on the feature with significant enhancement. Selected from fig. 6 are channel 1, channel 20, channel 38, and channel 54.
As can be seen from fig. 7 (the abscissa indicates the feature segment attention, i.e. the feature attention segment, and the ordinate indicates the number of samples of different fault types), the attention profile of the FFAE network is almost uniform for normal sample signals without any faults, with no particular attention to the feature segment. For the two types of faults which are difficult to distinguish between C1 and C3, the model focuses on the low-frequency part of the C1 fault signal and the high-frequency part of the C3 fault signal respectively, so that the model maximally distinguishes the two characteristics.
Fig. 8 shows the domain invariant feature for different conditions learned by the model, since the highlighting is the same, and the left and right graphs in fig. 8 are imbalance sample training data conditions 1 and 2 in order. Finally, from the above visual analysis of various features, it can be derived that the intelligent diagnostic model learns not the failure feature frequency with actual physical meaning, but features of a certain waveform signal that are statistically different from other waveform signals.
In summary, the invention can synchronously diagnose the bearing and the gear in an unbalanced sample, and simultaneously adopts a plurality of feature visualization methods, so that a learning mechanism in the intelligent diagnosis model is more interpretable, statistical feature information learned by the model is interpreted, and the application process of the intelligent diagnosis model is further promoted.
Claims (6)
1. An attention twin intelligent migration interpretability diagnostic method for high-end equipment, comprising the steps of:
S1, acquiring fault signals of a bearing and a gear in a high-end equipment simulation test bed, and obtaining a bearing fault sample and a gear fault sample;
Step S2, data preprocessing: sequentially combining the bearing data under three variable loads and the gear data under three variable rotation speeds, adding health samples, setting each type of health samples into different unbalanced sample sizes, performing time-frequency conversion on the unbalanced samples to convert the unbalanced samples into frequency domain signals, and performing standardized processing;
s3, constructing an attention twin intelligent migration interpretable diagnostic model
Step S3.1, building a double-branch convolutional neural network model, wherein the double-branch convolutional neural network model is divided into 5 convolutional layers, 5 pooling layers and a full-connection layer, and the model comprises a batch standardization layer and L2 regularization;
S3.2, designing a cross-module dual-attention mechanism module with cross modules connected in series, and establishing a feature visualization method of the corresponding module;
The cross-module dual-attention mechanism module comprises a channel feature attention module CFAE and a segment feature attention module FFAE, wherein the channel feature attention module CFAE is used for enhancing the feature values in different channels of the unbalanced sample, and the segment feature attention module FFAE is used for enhancing the feature values of different feature segments of the unbalanced sample;
S3.3, introducing a contrast loss function to construct a twin feature fusion network module;
s3.4, inserting a cross-module double-attention mechanism module and a twin feature fusion network module into the double-branch convolutional neural network model to construct an attention twin intelligent migration interpretable diagnostic model under an unbalanced sample; wherein after the CFAE module is placed at the 3 rd pooling layer, the FFAE module is placed at the 5 th convolution layer, and a twinning feature fusion network is applied at the global average pooling GAP layer;
Step S4, training an attention twin intelligent migration interpretable diagnostic model by using the unbalanced sample data obtained in the step S2, wherein the attention twin intelligent migration interpretable diagnostic model is trained by using unbalanced sample data under any two loads or rotating speeds, and the unbalanced sample data under two working conditions are firstly used for training;
S5, testing the diagnostic effect of the attention twin intelligent migration explanatory diagnostic model by using a test set; the internal weight and bias parameters of the trained attention twin intelligent migration interpretable diagnostic model are fixed, further, the diagnostic effect of the model is verified by using data under other rotating speeds or loads, a diagnostic comparison box graph and a confusion matrix with other models are drawn, and the performance of the model is displayed;
Step S6, visually displaying the statistical features learned by the attention twin intelligent migration interpretable diagnostic model by means of the cross-module double-attention mechanism module in the step S3, firstly displaying the enhancement of the channel features by the channel feature attention module CFAE, then displaying the enhancement of the features of different fragments by the fragment feature attention module FFAE, and simultaneously visually displaying the activation weight W2, so that the statistical feature information is specifically learned by the display model by combining with the spectrum waveform;
and S7, displaying cross-domain fusion and invariant features which are not changed along with working conditions in the unbalanced sample under the working conditions learned by the attention twin intelligent migration interpretability diagnostic model by means of the step S3 twin feature fusion network module, and further explaining feature information learned by the intelligent model.
2. The method for diagnosing the interpretability of the migration of the attention twin for high-end equipment according to claim 1, wherein the two-branch convolutional neural network model in the step S3.1 comprises 5 convolutional layers, 5 pooling layers and one full-connection layer, and further comprises a batch normalization layer and L2 regularization, and the formulas are as follows:
Where m represents the total amount of samples per batch, Sample representing each batch,/>And/>Represents the average and standard deviation, respectively,/>, of the batch dataAnd/>Respectively representing corrected sample values, wherein epsilon is used for avoiding the phenomenon that the denominator standard deviation is 0, and gamma and beta are respectively scale factors and translation factors;
Where E in is the original classification loss function, λ is the regularization parameter, and w represents the weight parameter in the model.
3. The attention twin smart migration interpretable diagnostic method for high-end equipment of claim 1, wherein the step S3.2 cross-module dual-attention mechanism module is detailed as follows: the expression of the channel feature attention module CFAE is:
Wherein CFAE is the feature output of the CFAE module, z (pool_3) represents the feature output of the third pooling layer, z activate represents the feature of attention activation, α is a correction coefficient, C represents the number of failure categories, and e is a constant;
The expression of the segment feature attention module FFAE is:
ffae＝zj,m(i)*W2＝zj,m(i)*[A1,A2,A3,A4,A5,A6,A7,A8]samples
Where ffae is the signature output of the FFAE module, W2 represents the activation weights for the different fragments of each sample, z j,m (i) is the signature output of the layer above FFAE, and a 1～A8 is the sample activation value for each signature fragment.
4. The attention twin smart migration interpretable diagnostic method for high-end equipment of claim 1, wherein the expression of the loss function in step S3.3 is:
Where ζ S represents the parameter of the contrast loss layer, L Siamese is the loss of the twinning feature fusion network, D (G 1,G2) represents the distance between unbalanced sample features from two speeds or loads (G 1,G2), Q is a constant, L represents the network layer, N represents the number, and N refers to the total number of samples.
5. The attention twin smart migration interpretable diagnostic method for high-end equipment of claim 1, wherein the total loss function of the attention twin smart migration interpretable diagnostic model obtained at step S3.4 is expressed as:
Lsum(ξe,ξS,ξL)＝Min[Lentropy(ξe)+LSiamese(ξS)+LL(ξL)]
Where N represents the number, C represents the number of failure categories, y is the sample label, p is the probability that the label is correct, L sum represents the total loss, ζ e represents the parameter of cross entropy loss, ζ L represents the parameter of regularization loss, L entropy is the classification loss cross entropy loss term, L L is the regularization loss term, and N represents the total number of samples.
6. The method for diagnosing the interpretive migration of attention twin for high-end equipment according to claim 1, wherein the enhanced values of the channel feature attention module CFAE for different channel features in step S6 are as follows:
Wherein z activate represents a value after feature attention activation, W1 is an attention weight, α is a correction coefficient, z (pool_3) represents a feature output by the third pooling layer, z activate represents a feature of attention activation, and C represents a failure category number;
the value of the segment feature attention module FFAE weight is formulated as follows:
Where W2 represents the activation weights for different segments of each sample, z j,m (i) is the feature output of the layer above FFAE, and A 1～A8 is the sample activation value for each feature segment.
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