Regrouping particle swarm optimization-based neural network for bearing fault diagnosis

被引：2
|
作者：
Liao, Yixiao [1 ]
Zhang, Lei [1 ]
Li, Weihua [1 ]
机构：
[1] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou, Guangdong, Peoples R China
来源：
2017 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC) | 2017年
基金：
中国国家自然科学基金;
关键词：
Fault Diagnosis; Regrouping Particle Swarm Optimization; Neural Network; ALGORITHM;
D O I：
10.1109/SDPC.2017.123
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate the performance of RegPSONN, bearing vibration data are used for verification. In addition, the back propagation neural network (BPNN), genetic algorithm based neural network (GANN) and particle swarm optimization neural network (PSONN) are used to classify the bearing data for algorithm comparison. Experimental results demonstrated that the proposed method was superior to other methods considering the classification rate.
引用
收藏
页码：628 / 631
页数：4
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