Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis. (15th February 2017)

• Record Type: Journal Article
• Authors: Cheng, Junsheng; Peng, Yanfeng; Yang, Yu; Wu, Zhantao
• Is Part Of: Mechanical systems and signal processing. Volume 85(2017)
• Page Start: 947
• Page End: 962
• Publication Date: 2017-02-15
• Subjects: Adaptive sparsest narrow-band decomposition -- Intrinsic narrow-band components -- Local narrow-band signal -- Singular local linear operator -- Rolling element bearing fault diagnosis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.09.024
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Enlightened by ASTFA method, adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. In ASNBD method, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate an intrinsic narrow-band component (INBC). ASNBD is proposed aiming at solving the problems existed in ASTFA. Gauss-Newton type method, which is applied to solve the optimization problem in ASTFA, is irreplaceable and very sensitive to initial values. However, more appropriate optimization method such as genetic algorithm (GA) can be utilized to solve the optimization problem in ASNBD. Meanwhile, compared with ASTFA, the decomposition results generated by ASNBD have better physical meaning by constraining the components to be local narrow-band signals. Comparisons are made between ASNBD, ASTFA and EMD by analyzing simulation and experimental signals. The results indicate that ASNBD method is superior to the other two methods in generating more accurate components from noise signal, restraining the boundary effect, possessing better orthogonality and diagnosing rolling element bearing fault. Highlights: Adaptive sparsest narrow-band decomposition method for fault diagnosis is proposed. ASNBD is compared with adaptive sparsest time-frequency analysis and EMD. Results show that ASNBD is suitable for non-stationary data analysis.