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FAULT DIAGNOSIS OF ROLLING BEARING BASED ON UNSUPERVISED FEATURE ALIGNMENT
Aiming at the problem that the characteristic distribution of rolling bearing vibration data collected under different speed environment is inconsistent and it is difficult to obtain the label of samples to be diagnosed, a fault diagnosis method based on deep migration network is proposed. In this model, a domain-shared feature extraction network is constructed. The convolutional neural network（CNN） is used to extract vibration signal sensitive fault features, and bi-directional Long short-term Memory is used to extract vibration signal sensitive fault features. BiLSTM) network to extract the time information of sensitive fault features; Then, CORAL loss and JMMD loss were embedded in the deep migration network, respectively. By minimizing the second-order statistical difference and the maximum mean difference of the joint distribution, the differences in the feature distributions of the source domain and target domain were reduced, and the common features of the two domains were extracted. Finally, add Softmax classification layer to realize fault status recognition of target data. The results show that the average recognition accuracy of this method is 97.87% when the target domain data is unlabeled, which is significantly higher than the other five popular adaptive fault diagnosis methods.
FAULT DIAGNOSIS OF ROLLING BEARING BASED ON UNSUPERVISED FEATURE ALIGNMENT
Aiming at the problem that the characteristic distribution of rolling bearing vibration data collected under different speed environment is inconsistent and it is difficult to obtain the label of samples to be diagnosed, a fault diagnosis method based on deep migration network is proposed. In this model, a domain-shared feature extraction network is constructed. The convolutional neural network（CNN） is used to extract vibration signal sensitive fault features, and bi-directional Long short-term Memory is used to extract vibration signal sensitive fault features. BiLSTM) network to extract the time information of sensitive fault features; Then, CORAL loss and JMMD loss were embedded in the deep migration network, respectively. By minimizing the second-order statistical difference and the maximum mean difference of the joint distribution, the differences in the feature distributions of the source domain and target domain were reduced, and the common features of the two domains were extracted. Finally, add Softmax classification layer to realize fault status recognition of target data. The results show that the average recognition accuracy of this method is 97.87% when the target domain data is unlabeled, which is significantly higher than the other five popular adaptive fault diagnosis methods.
FAULT DIAGNOSIS OF ROLLING BEARING BASED ON UNSUPERVISED FEATURE ALIGNMENT
ZHANG Tao (author) / JIA Qian (author) / XIN YueJie (author)
2022
Article (Journal)
Electronic Resource
Unknown
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ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE FEATURE SELECTION k-SUB CONVEX HULL
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