Bearing Fault Diagnosis Based on Full Vector-CNN
[1]HAO Wangshen,CHEN Yao,SUN Hao,et al.Bearing Fault Diagnosis Based on Full Vector-CNN[J].Journal of Zhengzhou University (Engineering Science),2020,41(05):92-96.[doi:10.13705/j.issn.1671-6833.2020.03.004]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41卷
Number of periods:
2020 05
Page number:
92-96
Column:
Public date:
2020-10-01
- Title:
- Bearing Fault Diagnosis Based on Full Vector-CNN
- Keywords:
- fault diagnosis; full vector spectrum; deep learning; convolutional neural network(CNN); rolling bearing
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2020.03.004
- Abstract:
- Aimed to improved at the traditional intelligent fault diagnosis system, which required a large amount of prior knowledge, and had the complexity of its model and the information loss caused by the incompleteness of single-channel signal, a full-vector deep convolutional neural network diagnosis model of rolling bearing was proposed. The full-vector technique was used to fuse the acquired two-channel signals to obtain the fused main vibration vector data, which contained more complete information than the single-channel data. Combining the main vibration vector and deep learning algorithm to construct the full vector depth convolutional neural network, the model could adaptively extract the fault features and use the back propagation algorithm to adjust the model parameters. The experimental results showed that the method could extract more complete fault information, and the model had higher accuracy and better stability.
- References:
-
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Last Update:
2020-10-23