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

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• 作者：Songrong Luo^a ; ^b ; Junsheng Cheng^a ; ^{signalp@tom.com" class="auth_mail" title="E-mail the corresponding author} ; Ming Zeng^a ; Yu Yang^a
• 关键词：Multi-scale higher order singular spectrum analysis ; Variable predictive model based class discriminate ; Genetic algorithm ; Rotating machinery ; Fault diagnosis
• 刊名：Measurement
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：87
• 期：Complete
• 页码：38-50
• 全文大小：831 K

文摘

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.