Application of BQGA-ELM network in the fault diagnosis of rolling bearings

A novel fault diagnosis model for rolling bearings, by the name of BQGA-ELM, was proposed based on the optimized extreme learning machine (ELM) combined with the Bloch spherical quantum genetic algorithm(BQGA). Comparing with other optimization algorithms including the genetic algorithm (GA), particle swarm optimization (PSO) and quantum genetic algorithm (QGA) by numerical simulations using the standard example data in the UCI machine learning repository: data sets, it is shown that the optimization method based on BQGA is superior to other optimization methods. The vibration signals of a rolling bearing in the following 4 cases, namely, normal, running, inner ring failure, outer ring fault and ball fault were collected in the lab, and the related characteristic parameters of the experimental data sets were extracted by time-domain analysis and then input into the diagnostic models. The diagnostic results show that the BQGA-ELM is a more reliable and suitable method than other methods for the defect diagnosis of rolling bearings, and its error convergency and fault diagnosis time are better than other diagnosis models in the paper. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.