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An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wang, Chun-Sheng Sha, Chun-Yang Hu, Yu-Kun |
| Copyright Year | 2017 |
| Abstract | An improved ensemble empirical mode decomposition (EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully. |
| Starting Page | 478 |
| Ending Page | 488 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/s11771-017-3450-8 |
| Alternate Webpage(s) | http://discovery.ucl.ac.uk/10066818/1/Hu_Manuscript.pdf |
| Alternate Webpage(s) | https://doi.org/10.1007/s11771-017-3450-8 |
| Volume Number | 24 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
