Abstract:

In this paper we apply the bat algorithm to solving the systemlevel fault diagnosis problem for the first time as an effective diagnosis algorithm. During the initialization phase, the population is divided into two categories: large and small, and they are handled in different ways. An equationconstrained fitness function is designed according to the characteristics of the systemlevel fault model. To balance global search and local search, a variable coefficient is added to the velocityupdating formula. We also perform binary mapping for bat speed to achieve the discretization of the addressing mode. Simulation results show that using the bat algorithm for fault diagnosis has significant advantages over FAFD, a typical representative of swarm intelligence diagnosis algorithms in aspects of the number of iterations, diagnostic accuracy and fitness of optimal solution.

Key words: system-level fault diagnosis;equation diagnosis algorithm;FAFD;bat algorithm