Universiti Teknologi Malaysia Institutional Repository

An improved wrapper-based feature selection method for machinery fault diagnosis

Hui, K. H. and Ooi, C. S. and Lim, M. H. and Leong, M. S. and Al-Obaidi, S. M. (2017) An improved wrapper-based feature selection method for machinery fault diagnosis. PLoS ONE, 12 (12). ISSN 1932-6203


Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.

Item Type:Article
Uncontrolled Keywords:vibration, algorithm
Subjects:Q Science > QH Natural history > QH301 Biology
ID Code:74838
Deposited By: Fazli Masari
Deposited On:07 Mar 2018 21:13
Last Modified:07 Mar 2018 21:13

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