基于模拟退火与LSSVM的轴承故障诊断

Bearing Fault Diagnosis Using Simulated Annealing Algorithm and Least Squares Support Vector Machines

运用模拟退火与最小二乘支持向量机(least square support vector machine,简称LSSVM)轴承的故障诊断法,是在得到较优的λ和σ参数的同时进行特征选择获取显著特征子集。为验证所提方法的有效性,将4种运行状态、5种转速、2类载荷条件下测得的轴承振动信号作为研究样本,提取信号的52个特征。试验结果表明,该法对轴承故障分类的准确率较高,可有效用于旋转机械的状态监控。

A fault diagnosis method based on the least square support vector machines(LSSVM) and the simulated annealing algorithm was proposed.Better parameters of the regularizing variable λ and the kernel width σ were obtained by using the simulated annealing algorithm,and the sensitive subset of features was determined simultaneously.To verify the effectiveness of the method,roller bearings were tested under four operating conditions,five different shaft speeds and two load levels,and 52 features were extracted from the bearing vibration signals.The results show that the method has a higher accuracy of classification for bearings fault than other methods,and it is a promising approach to condition monitoring of rotating machinery.