Fault Recognition Method of Rolling Bearing Based on Full Vector Sparse Coding
[1]Hao Wei,Lin Huiyi,Hao Wangshen,et al.Fault Recognition Method of Rolling Bearing Based on Full Vector Sparse Coding[J].Journal of Zhengzhou University (Engineering Science),2019,40(03):6-.[doi:10.13705/j.issn.1671-6833.2018.03.007]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40
Number of periods:
2019 03
Page number:
6-
Column:
Public date:
2019-04-30
- Title:
- Fault Recognition Method of Rolling Bearing Based on Full Vector Sparse Coding
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2018.03.007
- Abstract:
- Aiming at the feature shift phenomenon when using time domain signals to sparse coding and the problem of single channel analysis often result in inadequate use of information, a new method for fault identification of rolling bearings is proposed by combining the full vector spectrum technique and sparse coding. Firstly, full vector information fusion of the homogeneous dual-channel signal of the rolling bearing in each state is carried out. Then, the main vibration vector signals obtained is used to construct all kinds of redundant dictionaries. Finally, these dictionaries is used to reconstruct the test samples, and the error is used as the basis for identify the status of these samples. The method use the main vibration vector signals obtained by full vector fusion as training samples, so the training samples contain more comprehensive and accurate information. Besides, the feature extraction step can be eliminated, so it can reduce the effect of human factors. The experimental results show that the method has high efficiency and good stability, and can effectively identify the fault pattern of rolling bearing.
- References:
- -
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Last Update:
2019-04-16