Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6956318 | Mechanical Systems and Signal Processing | 2015 | 22 Pages |
Abstract
Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Huiming Jiang, Jin Chen, Guangming Dong, Tao Liu, Gang Chen,