The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for SVM. An artificial chemical reaction optimization algorithm (ACROA) is a new method to solve the global optimization problem and is adapted to optimize SVM parameters. In this paper, a SVM parameter optimization method based on ACROA (ACROA-SVM) is proposed. Furthermore, the ACROA-SVM is applied to diagnose roller bearing faults. Firstly, the original modulation roller bearing vibration signals are decomposed into product functions (PFs) by using the local mean decomposition (LMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some PFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios are used as input to the ACROA-SVM classifiers, and the fault patterns of the roller bearing are identified. The result shows that the combination of this ACROA-SVM classifiers and LMD method can effectively improve the accurate rate of fault diagnosis and reduce cost time.