机械制造与自动化2024，Vol.53Issue(3)：45-49,5.DOI:10.19344/j.cnki.issn1671-5276.2024.03.009
基于粒子群算法优化BP神经网络的轴承故障诊断
Bearing Fault Diagnosis Based on PSO-BP Neural Network
摘要
Abstract
PSO algorithm is applied to optimize the weight and threshold of BP neural network and conduct the fault diagnosis of rolling.The acceleration data of driving end and the acceleration data of fan end are taken as input to ouput three different states of bearing by training network,so as to realize the fault diagnosis of bearing.The simulation results show that the network model can accurately identify the running state and fault type of bearings,and the test accuracy of normal samples reaches 98％.Compared with BP neural network,the test accuracy is greatly improve with stronger generalization ability and higher feasibility.关键词
轴承/故障诊断/BP神经网络/粒子群算法Key words
bearing/fault diagnosis/BP neural network/PSO分类
机械制造引用本文复制引用
樊怀聪,田禾,冯明文,曹冉冉..基于粒子群算法优化BP神经网络的轴承故障诊断[J].机械制造与自动化,2024,53(3):45-49,5.基金项目
国网天津市电力公司科技项目(KJ21-1-21) （KJ21-1-21）
