Author(s): ZHAO Hai-yang, XU Min-qiang, WANG Jin-dong, School of Astronautics, Harbin Institute of Technology, College of Mechanical Science and Engineering, Northeast Petroleum University
Pages: 105-109
Year: 2013 Issue:  23
Journal: Journal of Vibration and Shock
Keyword:  multifractal; singular value decomposition; clearance fault; support vector machine; reciprocating compressor; fault diagnosis;
Abstract: Here,a fault feature extraction method based on multifractal and singular value decomposition of multisensor was presented,aiming at interference and coupling of fault information and complex non-linear,and non-stationary characteristics of vibration signals in a reciprocating compressor. The generalized fractal dimension number could characterize local scale behavior of a signal more appropriately,so an initial feature matrix was built by calculating the generalized fractal dimension number of multi-sensor signals. The matrix was compressed with the singular value decomposition method,and its eigenvalues were taken as feature vectors. Taking a reciprocating compressor transmission mechanism as a study object,feature vectors of bearing clearance faults of different positions were extracted from vibration signals. A support vector machine was established as a pattern classifier to identify faults. Compared with results of the single sensor multifractal method and the multi-sensor single fractal method,the validity of this proposed method was verified.