Multiple fault diagnosis using psycho-clonal algorithms

Multiple Fault Diagnosis (MFD) is used as an effective way to tackle the problems of a real shop fl oor environment in order to reduce the total lifetime maintenance costs of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases; thus, it warrants the application of heuristic techniques or artifi cial intelligence (AI) based optimization tools to diagnose the exact faults in real time. In this chapter, a methodology based on a Probabilistic Causal Model has been illustrated to resolve graph based multiple fault diagnosis problems. This methodology involves a new nature inspired algorithm know as the psycho-clonal algorithm for fault diagnosis. In the proposed methodology, we collect the faults corresponding to each observed manifestation that can give the best possible result instead of fi nding all possible combinations of faults. Intensive computational experiments on well-known data sets witness the superiority of the proposed psycho-clonal algorithms over earlier approaches existing in the literature. From experimental results, it is observed that the proposed methodology can diagnose the exact fault in the minimum fault isolation time as compared to other approaches.