A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Autor:	Mingliang Liang, Dongmin Su, Daidi Hu, Mingtao Ge
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Physics QC1-999
Zdroj:	Shock and Vibration, Vol 2018 (2018)
Druh dokumentu:	article
ISSN:	1070-9622 1875-9203
DOI:	10.1155/2018/1891453
Popis:	A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.
Databáze:	Directory of Open Access Journals
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