关键词: 经验模式分解, K-L散度, 粒子群算法, 支持向量机, 齿轮故障诊断

Abstract: For the problem that the characterization of the gear fault signal feature is difficult to extract and the structure parameters selection of Support Vector Machine (SVM) are based on experience leads the poor precision of fault state recognition,proposes a K-L divergence and PSO-SVM based method of gear fault diagnosis.First of all,the gear vibration signal is divided by EMD into several Intrinsic Mode Functions (IMF).Then,it selects IMF that contains main characteristics of signal and calculates their K-L divergence with the original signal value.Second,the Particle Swarm Optimization (PSO) was used to optimize the punish coefficient of Support Vector Machine (SVM) and the structural parameters of Gaussian kernel width coefficient.The gear fault classification model is built;The effectiveness of the method was validated by the experimental data of gear.The experimental result shows that compared with the TF-SVM,TF-PSO-SVM,gear fault diagnosis method based on K-L divergence and PSO-SVM has higher precision.

Key words: Empirical Mode Decomposition(EMD), K-L divergence, Particle Swarm Optimization(PSO), Support Vector Machine(SVM), gear fault diagnosis