An Unsupervised Bearing Fault Diagnosis Based on Deep Subdomain Adaptation Under Noise and Variable Load Condition

Abstract Deep learning-based approaches for diagnosing bearing faults have attracted considerable attention in the last years. However, in real-world applications, these methods face challenges. For proper training of these models, a considerable amount of labeled data are necessary, and due to limitations in industry, obtaining this amount of data may not be possible. Because of load variations, the distribution of training and test data may vary, which reduces the accuracy of the trained model for various working conditions. Furthermore, noise has a signiﬁcant impact on bearing fault diagnosis performance in real-world industrial applications. This study introduced the deep subdomain adaptation convolutional neural network (DSACNN) method to overcome these challenges in real scenarios. The local maximum mean discrepancy (LMMD) method reduces the difference between each class distribution in the source and target domains. We validated our proposed method by CWRU bearing dataset under various loads and noise with different SNRs. The results show that DSACNN outperforms other comparative methods in anti-noise performance and reduction of domain’s distribution discrepancies.

Download Full-text

A new bearing fault diagnosis method using elastic net transfer learning and LSTM

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210503 ◽

2021 ◽

pp. 1-9

Author(s):

Xudong Song ◽

Dajie Zhu ◽

Pan Liang ◽

Lu An

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Working Conditions ◽

Short Term Memory ◽

Elastic Net ◽

Variable Load ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Model ◽

Net Transfer

Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method.

Download Full-text

Intelligent Diagnosis of Subway Traction Motor Bearing Fault Based on Improved Stacked Denoising Autoencoder

Shock and Vibration ◽

10.1155/2021/6656635 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yanwei Xu ◽

Chen Li ◽

Tancheng Xie

Keyword(s):

Fault Diagnosis ◽

Working Conditions ◽

Vibration Signal ◽

Variable Load ◽

Denoising Autoencoder ◽

Traction Motor ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Feature Values ◽

Motor Bearing

Aiming at the problem that the complex working conditions affect the effect of manual feature extraction in bearing fault diagnosis of metro traction motor, a fault diagnosis method of metro traction motor bearing based on improved stacked denoising autoencoder (SDAE) is proposed. This method extracts fault features directly from the original vibration signal through deep learning, reduces the dependence on signal processing technology and diagnosis experience, and solves the problem of unsatisfactory effect of extracting feature values under complex working conditions. The effect of the improved SDAE network structure on the accuracy of bearing fault diagnosis is studied through experiments, and the best network parameters are selected. The test results show that the proposed method can well extract the deep features of the fault under the condition of variable speed and variable load; when using data sets with complex working conditions, the classification accuracy of the proposed method is better than that of many traditional fault diagnosis methods.

Download Full-text