Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

Autor: Jiang Xingmeng, Wu Li, Pan Liwu, Ge Mingtao, Hu Daidi
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: Journal of Engineering, Vol 2016 (2016)
Druh dokumentu: article
ISSN: 2314-4904
2314-4912
DOI: 10.1155/2016/1308108
Popis: Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.
Databáze: Directory of Open Access Journals
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