Few-shot Learning for Rolling Bearing Fault Diagnosis Based on Residual Convolutional Neural Network

As the degradation of bearing yield to an enormous adverse impact on machinery and the damage that comes within could jeopardize human precious life. Hence, the bearing fault diagnosis is indisputably indispensable. This paper is predominantly focused on the utilization of Convolutional Neural Network (CNN) in bearing fault diagnosis of the rolling bearing. By deployment of CNN, an accurate diagnosis can be achieved without the necessity of pre-training the data. The function of CNN in diagnosing the bearing and architecture development of CNN are discussed. Lastly, to establish new and significant contribution in this area, new challenges are pinpointed.

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Rolling Bearing Fault Diagnosis Method Based on Stacked Denoising Autoencoder and Convolutional Neural Network

2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE) ◽

10.1109/qr2mse46217.2019.9021126 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yumin Wang ◽

Minghong Han ◽

Wei Liu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Denoising Autoencoder ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method

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Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network

IEEE Access ◽

10.1109/access.2020.3016888 ◽

2020 ◽

Vol 8 ◽

pp. 150248-150261

Author(s):

Zhihao Chen ◽

Jian Cen ◽

Jianbin Xiong

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Frequency Analysis ◽

Rolling Bearing ◽

Time Frequency Analysis ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

Applied Sciences ◽

10.3390/app10030770 ◽

2020 ◽

Vol 10 (3) ◽

pp. 770 ◽

Cited By ~ 1

Author(s):

Guoqiang Li ◽

Chao Deng ◽

Jun Wu ◽

Zuoyi Chen ◽

Xuebing Xu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Wavelet Packet ◽

Rolling Bearing ◽

Wavelet Packet Transform ◽

Fault Classification ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

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