An evolutionary approach to system-level fault diagnosis

Authors:
Hui Yang;Mourad Elhadef;Amiya Nayak;Xiaofan Yang
Affiliations:
College of Computer Science, Chongqing University, Chongqing, China;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;College of Computer Science, Chongqing University, Chongqing, China
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

Artificial immune systems (AIS) have been widely applied to many fields such as data analysis, multimodal function optimization, error detection, etc. In this paper, we show how AIS can be used for system-level fault diagnosis. Experimental results from a thorough simulation study and theoretical analysis demonstrate the effectiveness of the AIS-based diagnosis approach for different small and large systems in both the worst and average cases, making it a viable addition to the existing diagnosis algorithms.