A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. (March 2016)

• Record Type: Journal Article
• Authors: Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun
• Is Part Of: ISA transactions. Volume 61(2016:Mar.)
• Page Start: 211
• Page End: 220
• Publication Date: 2016-03
• Subjects: Rotating machinery -- Fault diagnosis -- Second generation wavelet de-noising -- Local mean decomposition -- Signal–noise ratio; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2015.12.009
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. Highlights: A rotating machinery hybrid fault diagnosis method using SGWD and LMD is proposed. SGWD can remove noise and enhance the SNR in rotating machinery vibration signals. Results demonstrate that the proposed method has better performances than LMD.