Title: A swarm-based multiple reduction approach for fault diagnosis

Authors: Fengqiang Zhao; Hongbo Liu

Addresses: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China; College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian 116600, China ' School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Abstract: Fault diagnosis is a complex and difficult problem in the equipment maintenance. Whenever a fault symptom is detected, the system diagnosis is expected to carry out timely. It would be very helpful to improve the overall productivity. This paper presents a class of fault diagnosis problem, in which many items could be chosen in the diagnosis environment. Some of the data come from their experience or estimation. The information is redundant and inaccurate. Swarm-based rough set approach is introduced to make an attempt to solve the problem. Rough set theory provides a mathematical tool that can be used for both feature selection and information reduction. The swarm-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multiple fault diagnosis planning and to improve diagnosis the decision. Empirical results illustrate that the approach can be applied for the class of fault diagnosis problems effectively.

Keywords: swarm algorithm; swarm intelligence; rough sets; fault diagnosis; equipment maintenance; discrete PSO; particle swarm optimisation.

DOI: 10.1504/IJMIC.2013.052820

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.3, pp.261 - 267

Published online: 16 Aug 2014 *

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