| Summary: | 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 and generalization ability of fault state recognition,a method that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value is proposed. Firstly,the rolling bearing signal is divided by EMD,it selects IMFs that contains main characteristics of signal for Hilbert demodulation envelope analysis to obtain envelope matrix and the singular value decomposition is carried out. Secondly,the IPSO algorithm is used to optimize the penalty coefficient and the structural parameters of SVM to set up the rolling bearing fault classification model. And by using the bearing data of Case Western Reserve University,the validity of the method is verified. The 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.
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