Rolling bearing fault diagnosis method based on DBN optimized by ISFLA

QI Hong-fang1, HUANG Ding-hong2
(1.School of Intelligent Manufacturing, Wuhan Huaxia University of Technology, Wuhan 430223, China;
2.School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract: Aiming at the problems that the traditional fault diagnosis model was easy to fall into local optimality, the model generalization ability was poor, and the fault recognition accuracy was easily affected by the quality of artificial feature extraction, the fault diagnosis method of rolling bearing was studied. Firstly, a fault diagnosis model of rolling bearing based on deep belief network (DBN) was proposed and the hierarchical adaptive feature extraction capability of DBN model was studied. Then, an improved shuffled frog algorithm (ISFLA) was designed to optimize the number of neurons in each hidden layer and the learning rate of reverse fine-tuning algorithm. Finally, the bearing data set of Case Western Reserve University was used for experimental research without any feature extraction of the data. In the experiment, the original timedomain vibration signal was extracted, and the fault features were analyed and compared with the BP, DBN and PSO-DBN algorithms. The results show that ISFLA-DBN has the highest fault recognition accuracy, the fastest algorithm convergence rate, and the best model generalization ability.

Key words: rolling bearing; fault diagnosis; deep belief network (DBN); improved shuffled frog leaping algorithm (ISFLA)