一种基于概率盒-PSO-SVM的滚动轴承故障诊断方法

摘要

针对滚动轴承故障诊断的问题,提出了一种基于概率盒理论和粒子群优化支持向量机的故障诊断新方法.在分析故障信号的概率统计特性基础上,利用概率盒直接建模方法获得概率盒,利用证据理论实现了概率盒的融合.不同故障状态下的概率盒特征也不同,采用不同的累积不确定性测量方法提取了概率盒的特征,并构建出用于模式识别的特征向量集,将特征集代入利用粒子群算法优化后的支持向量机中实现故障诊断.通过对滚动轴承振动信号的实验测试与对比分析表明:该方法可以实现对滚动轴承准确的诊断,与传统特征提取方法对比,证明了方法的有效性.%A method for rolling bearing fault diagnosis based on the probability box (p-box) and support vector machine (SVM) with particle swarm optimization (PSO) algorithm was proposed.Pboxes were obtained by using the direct p-box modeling method based on the probability and statistics analysis of fault signals' characteristics,and the p-boxes fusion was realized by using the evidence theory.P-boxes features are different under different fault conditions,so,the features of p-boxes were extracted by different methods of p-box cumulative uncertainty measurement.A feature vector set for pattern recognition was constructed,which was then brought into the SVM whose key parameters were optimized by the PSO algorithm to realize the fault diagnosis.The experimental results indicate that the method can be used to accurately diagnose the rolling bearing faults.Comparing with the traditional feature extraction methods,the validity of the method was proved.