An intelligent fault diagnosis model for rotating machinery based on multi-scale higher order singular spectrum analysis and GA-VPMCD

Feature extraction and class discrimination are two key problems for fault diagnosis of rotating machinery. Firstly, multi-scale higher order singular spectrum analysis (MS-HO-SSA) method is presented and the multi-scale higher order singular spectrum entropy (MSHOSSE) is defined as feature to reveal the non-Gaussian and nonlinear characteristic for the vibration signals from rotating machinery with local faults. Secondly, GA-VPMCD method is presented by combination genetic algorithm (GA) with conventional variable predictive model based class discriminate (VPMCD) approach. Lastly, an intelligent fault diagnosis model based on MS-HO-SSA and GA-VPMCD is put forward and utilized for rotor fault diagnosis. The experimental results show that MS-HO-SSA method is more effective for feature extraction and the GA-VPMCD provides better performance than conventional VPMCD and LSSVM.