Research Article
A Novel PCA-DBN Based Bearing Fault Diagnosis Approach
@INPROCEEDINGS{10.1007/978-3-030-32388-2_39, author={Jing Zhu and Tianzhen Hu}, title={A Novel PCA-DBN Based Bearing Fault Diagnosis Approach}, proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings}, proceedings_a={MLICOM}, year={2019}, month={10}, keywords={PCA DBN Rolling element bearing Fault diagnosis}, doi={10.1007/978-3-030-32388-2_39} }- Jing Zhu
Tianzhen Hu
Year: 2019
A Novel PCA-DBN Based Bearing Fault Diagnosis Approach
MLICOM
Springer
DOI: 10.1007/978-3-030-32388-2_39
Abstract
This paper is concerned with fault diagnosis problem of a widely used component in vast rotating machinery, rolling element bearing. We propose a novel intelligent fault diagnosis approach based on principal component analysis (PCA) and deep belief network (DBN) techniques. By adopting PCA technique, the dimension of raw bearing vibration signals is reduced and the bearing fault features are extracted in terms of primary eigenvalues and eigenvectors. Parts of the modified samples are trained by DBN for fault classification and diagnosis and the rest are tested to examine the algorithm. A distinctive feature of this approach is that it requires no complex signal processing procedure of bearing vibration signals. The experimental results demonstrate the effectiveness of the PCA-DBN based fault diagnosis approach with a more than 90% accuracy rate.
