DOAJ Open Access 2016

Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

Jiang Xingmeng Wu Li Pan Liwu Ge Mingtao Hu Daidi

Abstrak

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.

Topik & Kata Kunci

Engineering (General). Civil engineering (General)

Penulis (5)

Jiang Xingmeng

Wu Li

Pan Liwu

Ge Mingtao

Hu Daidi

Format Sitasi

APA MLA BibTeX

Xingmeng, J., Li, W., Liwu, P., Mingtao, G., Daidi, H. (2016). Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM. https://doi.org/10.1155/2016/1308108

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Informasi Jurnal

Tahun Terbit: 2016
Sumber Database: DOAJ
DOI: 10.1155/2016/1308108
Akses: Open Access ✓