An intelligent fault diagnosis model for rotating machinery based on multi-scale higher order singular spectrum analysis and GA-VPMCD. (June 2016)

• Record Type: Journal Article
• Authors: Luo, Songrong; Cheng, Junsheng; Zeng, Ming; Yang, Yu
• Is Part Of: Measurement. Volume 87(2016:Jun.)
• Page Start: 38
• Page End: 50
• Publication Date: 2016-06
• Subjects: Multi-scale higher order singular spectrum analysis -- Variable predictive model based class discriminate -- Genetic algorithm -- Rotating machinery -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.01.006
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: 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.