Fault diagnosis of dynamical systems using evolutionary algorithms

Challenging and complex design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques cannot often provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms are becoming more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms in fault diagnosis. The main attention is on the techniques that integrate the classical and evolutionary approaches. A selected example, dealing with the DAMADICS benchmark, is carefully described in the paper.