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

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.

scholarly journals One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6039
Author(s):  
Kai Wang ◽  
Wei Zhao ◽  
Aidong Xu ◽  
Peng Zeng ◽  
Shunkun Yang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Target Domain ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Classifier Adaptation ◽  
Feature Adaptation

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

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

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.

A novel transfer learning method for bearing fault diagnosis under different working conditions

Measurement ◽  
2021 ◽  
Vol 171 ◽  
pp. 108767
Author(s):  
Yisheng Zou ◽  
Yongzhi Liu ◽  
Jialin Deng ◽  
Yuliang Jiang ◽  
Weihua Zhang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Learning Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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