Affiliations: School of Mechanical and Automotive Engineering, South China University of Technology, TianHe, Guangzhou, China
Correspondence:
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Corresponding author. Weihua Li, School of Mechanical and Automotive Engineering, South China University of Technology, No. 381 Wushan RD, TianHe District, Guangzhou, China. E-mail: [email protected].
Abstract: Machine learning based intelligent diagnosis methods can adaptively generate the fault diagnosis model by historical data, which have attracted much attention. Artificial neural network (ANN) is one of the most important tools for gearbox intelligent diagnosis. However, the training of ANN has the problem of local optima, and it is hard to determine the ANN structure. These problems have great influence on the diagnosis performance of ANN. In this paper, a variable neural network (RegPSOVNN) is proposed for gearbox fault diagnosis based on regrouping particle swarm optimization. Ten time-domain features are selected to form the input of the ANN. Regrouping particle swarm optimization (RegPSO) is utilized for the optimization of ANN structure and network training. It can simultaneously optimize the structure and parameters of ANN and effectively avoid the problem of local optima. To evaluate the diagnosis performance of the proposed method, gearbox failure experiments were conducted, and backpropagation neural network (BPNN), firefly variable neural network (FAVNN) and particle swarm optimization based variable neural network (PSOVNN) were used for comparison. Experimental results indicated that the proposed method can effectively optimize the network structure and diagnosis the gearbox faults.
