A multi-population state optimization algorithm for rail crack fault diagnosis

Abstract

Rails usually operate in complex environments, which makes them prone to mechanical failures. In order to better diagnose crack faults, a multi-population state optimization algorithm (MPVHGA) is proposed in this paper, which is used to solve the problems of low efficiency, easy precocity, and easy convergence of local optimal solutions in traditional genetic algorithms. The fault signal detection results show that the MPVHGA has the advantages of fast convergence rate, high stability, no stagnation, and no limitation of the number of fixed iterations. The average iterations number of MPVHGA in 100 independent iterations is about one-fifth of the traditional single genetic algorithm (SGA for short) and about one-third of the population state optimization algorithm (VHGA for short), and the total convergence number of the MPVHGA converges to 55 and 10 more than the SGA and VHGA, respectively, and the accuracy of its fault diagnosis can reach 95.04%. On the basis of improving the performance of simple genetic algorithms, this paper provides a new detection method for rail crack fault diagnosis, which has important practical value in engineering.