HHT-Singular Value and IPSO-SVM in Fault Diagnosis of Rolling

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, this paper proposes a method that IPSO-SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value. Second, IPSO algorithm was used to optimize the penalty coefficient and the structural parameters of SVM to set up the rolling bearing fault classification model. First, the signal is divided by EMD. Then it selects IMFs that contains main characteristics of signal for Hilbert demodulation envelope analysis to obtain envelope matrix and do the singular value decomposition. And using the case western reserve university bearing data verify the validity of the method. Experimental results show that IPSO-SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value compared with the fault classification model based on BP, SVM has higher precision and stronger generalization ability.