Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion

Ridha Ziani (), Ahmed Felkaoui and Rabah Zegadi
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Ridha Ziani: Ferhat Abbes University Setif 1
Ahmed Felkaoui: Ferhat Abbes University Setif 1
Rabah Zegadi: Ferhat Abbes University Setif 1

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 2, No 9, 405-417

Abstract: Abstract Condition monitoring of rotating machinery has attracted more and more attention in recent years in order to reduce the unnecessary breakdowns of components such as bearings and gears which suffer frequently from failures. Vibration based approaches are the most commonly used techniques to the condition monitoring tasks. In this paper, we propose a bearing fault detection scheme based on support vector machine as a classification method and binary particle swarm optimization algorithm (BPSO) based on maximal class separability as a feature selection method. In order to maximize the class separability, regularized Fisher’s criterion is used as a fitness function in the proposed BPSO algorithm. This approach was evaluated using vibration data of bearing in healthy and faulty conditions. The experimental results demonstrate the effectiveness of the proposed method.

Keywords: Support vector machines (SVMs); Particle swarm optimization (PSO); Regularized linear discriminant analysis (RLDA); Features selection; Condition monitoring (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-014-0987-3

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