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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jianjun Zhang Zhongsheng Wang Jiancang Ma |
| Copyright Year | 2007 |
| Description | Author affiliation: Northwestern Polytech. Univ., Xian (Jianjun Zhang; Zhongsheng Wang; Jiancang Ma) |
| Abstract | A signal adaptive time-frequency decomposition to extend matching pursuit algorithm-probabilistic matching pursuit is studied. This method is firstly introduced into early fault diagnosis of rolling bearing. It adopts Gaussian wavelet to construct the time-frequency atoms dictionary. Applying probabilistic matching pursuit, we can accurately extract the early defect features by computing coherence between vibration signals of rolling bearing with inner or outer race fault and the dictionary elements. Comparing with matching pursuit, probabilistic matching pursuit represents vibration signal features with very fine resolution and sparsity in the time-frequency domain through computer simulations. Simultaneously, experiment results confirm that probabilistic matching pursuit is quite effective to facilitate the early diagnosis and identification of rolling bearing faults. |
| Starting Page | 251 |
| Ending Page | 251 |
| File Size | 486795 |
| Page Count | 1 |
| File Format | |
| ISBN | 0769528821 |
| DOI | 10.1109/ICICIC.2007.278 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-05 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault diagnosis Portable media players Dictionaries Continuous wavelet transforms Fault detection Matching pursuit algorithms Rolling bearings Time frequency analysis Discrete wavelet transforms Pursuit algorithms |
| Content Type | Text |
| Resource Type | Article |
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