An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

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 loco...

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Published in:Journal of Central South University 2017-02, Vol.24 (2), p.478-488
Main Authors: Wang, Chun-sheng, Sha, Chun-yang, Su, Mei, Hu, Yu-kun
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Algorithms
Bearing
Computer simulation
Engineering
Metallic Materials
Sifting
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cited_by cdi_FETCH-LOGICAL-c452t-54704fbe56e5a7dfd6e0ffee3b4585337f542eb309bd5602564b99e6e63907c63
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description 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.
doi_str_mv 10.1007/s11771-017-3450-8
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subjects Adaptive algorithms
Adaptive filters
Algorithms
Bearing
Computer simulation
Engineering
Metallic Materials
Sifting
title An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter
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