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An improved wrapper-based feature selection method for machinery fault diagnosis
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 12, p e0189143 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- 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.
- Subjects :
- 0209 industrial biotechnology
Computer and Information Sciences
Support Vector Machine
Computer science
lcsh:Medicine
Feature selection
02 engineering and technology
computer.software_genre
Research and Analysis Methods
Vibration
Skewness
Machine Learning
Machine Learning Algorithms
020901 industrial engineering & automation
Artificial Intelligence
Support Vector Machines
0202 electrical engineering, electronic engineering, information engineering
lcsh:Science
Statistical Data
Damage Mechanics
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Physics
lcsh:R
Classical Mechanics
Probability Theory
Probability Distribution
Support vector machine
Equipment Failure Analysis
Rolling-element bearing
Physical Sciences
020201 artificial intelligence & image processing
lcsh:Q
Data mining
Classifier (UML)
computer
Mathematics
Algorithms
Statistics (Mathematics)
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....641984de84336c008347b6f1f2b1c7c8