A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

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Rolling Bearing Fault Diagnosis Based on Meta-Learning with Few-Shot Samples

10.1109/iai53119.2021.9619308 ◽

2021 ◽

Author(s):

Yunpeng He ◽

Chuanzhi Zang ◽

Peng Zeng ◽

Mingxin Wang ◽

Qingwei Dong ◽

...

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Meta Learning

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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.

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