基于熵带法与PSO优化的SVM转子故障诊断

Fault Diagnosis for Rotor Systems Based on Entropy Band Method and Support Vector Machine Optimized by PSO

对转子故障信号的信息熵带作为支持向量机(support vector machine,简称SVM)的训练样本,基于粒子群算法(particle swarm optimization,简称PSO)优化SVM分类器结构参数进行了研究。对试验模拟获得的故障信号进行了时域、频域、时-频域的信息熵带计算,得到了奇异值谱熵、功率谱熵、小波空间谱熵及小波能谱熵4种熵带,并对熵带进行预处理,建立了一种基于故障信号的信息熵带作为特征量,用PSO解决SVM结构参数优化设置的转子故障识别方法。将该方法应用于转子系统在线故障诊断中,结果表明,所设计的算法具有训练速度快、测试时间短、分类准确率高等特点。

How to optimize structure parameters of support vector machine(SVM) classifier is reseached based on particle swarm optimization(PSO).The information entropy band of the fault signal is used as SVM training sample.Singular spectrum entropy,power spectrum entropy,wavelet energy spectrum entropy and wavelet space feature entropy values are obtained after computing the entropy band value of fault signal in the time,frequency and time-frequency domains.A fault identification method for the rotor system is established based on SVM,whose structure parameters are optimized by PSO training features of SVM are the four entropy band values processed before.Experimental results show that the designed classifier has many advantages of high training speed,short test time and high classification accuracy on fault diagnosis for rotor systems online.