A swarm-based multiple reduction approach for fault diagnosis

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.