A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery. (15th September 2017)

• Record Type: Journal Article
• Authors: Zhao, Ming; Jia, Xiaodong
• Is Part Of: Mechanical systems and signal processing. Volume 94(2017)
• Page Start: 129
• Page End: 147
• Publication Date: 2017-09-15
• Subjects: Singular value decomposition -- Periodic modulation intensity -- Signal denoising -- Weak feature enhancement -- Reweighted singular value decomposition; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.02.036
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The reason for the failure of traditional SVD denoising is investigated. PMI is proposed to quantity the informativeness of a mechanical signal. RSVD can extract weak fault feature under large interferences and noise. The performance is validated by denoising sound and vibration signals. Abstract: Singular value decomposition (SVD), as an effective signal denoising tool, has been attracting considerable attention in recent years. The basic idea behind SVD denoising is to preserve the singular components (SCs) with significant singular values. However, it is shown that the singular values mainly reflect the energy of decomposed SCs, therefore traditional SVD denoising approaches are essentially energy-based, which tend to highlight the high-energy regular components in the measured signal, while ignoring the weak feature caused by early fault. To overcome this issue, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement. In this work, a novel information index called periodic modulation intensity is introduced to quantify the diagnostic information in a mechanical signal. With this index, the decomposed SCs can be evaluated and sorted according to their information levels, rather than energy. Based on that, a truncated linear weighting function is proposed to control the contribution of each SC in the reconstruction of the denoised signal. In this way, some weak but informative SCs could be highlighted effectively. The advantages of RSVD over traditional approaches are demonstrated by both simulated signals and real vibration/acoustic data from a two-stage gearbox as well as train bearings. The results demonstrate that the proposed method can successfully extract the weak fault feature even in the presence of heavy noise and ambient interferences.