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

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 th...

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Veröffentlicht in:PloS one Jg. 12; H. 12; S. e0189143
Hauptverfasser: Hui, Kar Hoou, Ooi, Ching Sheng, Lim, Meng Hee, Leong, Mohd Salman, Al-Obaidi, Salah Mahdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 20.12.2017
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Artificial intelligence
Automation
Bearing
Classification
Colleges & universities
Computation
Computer and Information Sciences
Computer applications
Data collection
Diagnosis
Equipment Failure Analysis
Evolution & development
Fault diagnosis
Knowledge
Learning algorithms
Machine learning
Machinery
Machinery and equipment
Mechanical engineering
Medical diagnosis
Methods
Noise
Physical Sciences
Redundancy
Research and Analysis Methods
Skewness
Support Vector Machine
Support vector machines
Vector analysis
Vibration
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0189143