关键词: 内燃机, 故障诊断, 时频分析, 特征融合, 识别率

Abstract: Aiming at the problems of fault identification and diagnosis based on the IC engine vibration signals, a time-frequency analysis method was proposed based on variational mode decomposition(VMD) threshold filtering，Margenau-Hill time-frequency distribution(MHD). The method aimed at the problems of cross-interference terms of the Cohen time-frequency distribution, determining decomposition level of the VMD algorithm by the threshold filtering method, the IC engine vibration signals were decomposed into a series of single-component modal signals; then the MHD time-frequency characterization and linear superposition were applied to the single-component signals to get the time-frequency images of the vibration signals with good time-frequency clustering and clear physical meaning.Then，the local non-negative matrix factorization(LNMF) was used to extract features of time-frequency images，and the extracted features were combined with the time domain parameters of the vibration signals to obtain the fused feature vectors. The improved particle swarm optimization algorithm was used to optimize the parameters of the support vector machine(SVM)，and then the feature vectors were trained and tested by SVM to realize the fault identification and diagnosis of the IC engines. The method was applied to the identification and diagnosis tests of the cylinder cover vibration signals under 8 working conditions of valve clearances. The results show that the method may effectively identify and classify the vibration signals of different working conditions. Through parameter optimization, the highest recognition rate reaches 99.17%，and compared the classification results of the traditional nearest neighbor classifier，the superiority of the method is proved.

Key words: internal combustion（IC） engine, fault diagnosis, time-frequency analysis, feature fusion, recognition rate