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

IEEE Access ◽  
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

scholarly journals Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4827 ◽  
Author(s):  
Hengchang Liu ◽  
Dechen Yao ◽  
Jianwei Yang ◽  
Xi Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Domain Signal ◽  
The One ◽  
Short Time

The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.

An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image

Measurement ◽  
2020 ◽  
Vol 157 ◽  
pp. 107667 ◽  
Author(s):  
Ying Zhang ◽  
Kangshuo Xing ◽  
Ruxue Bai ◽  
Dengyun Sun ◽  
Zong Meng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 137395-137406 ◽  
Author(s):  
Laohu Yuan ◽  
Dongshan Lian ◽  
Xue Kang ◽  
Yuanqiang Chen ◽  
Kejia Zhai
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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