Wavelet sparsity enhancement for extracting transient vibration signatures of bearing structural damages

Abstract

Wavelet methods are widely used in mechanical transient vibration signature detection and fault diagnosis. Undesirable artifacts (e.g., spurious noise spikes and pseudo-Gibbs components) are serious issues for the mainstream methods, probably resulting in inaccurate analysis results. For this reason, a new wavelet sparsity enhancement methodology is proposed to achieve artifact-free extraction of bearing transient vibration signatures. The wavelet sparsity is enhanced through two aspects—that is, wavelet basis design and wavelet coefficient processing. Specifically, we use a new family of wavelets called fractional B-spline wavelets on bearing fault signal analysis and propose the adaptive undecimated fractional spline wavelet transform which addresses the shift-invariant problem and also permits to customize an optimal wavelet basis according to the signal itself adaptively. Meanwhile, we introduce a two-step wavelet processing method including a nonlinear operator and a generalized hard-thresholding (with symmetric or asymmetric thresholds determined automatically by wavelet coefficients at each level). Moreover, unlike the most existing wavelet methods, the approximation coefficients are also processed along with the detailed coefficients to remove the possible low-frequency noise. The final transient vibration signatures are reconstructed with the processed coefficients and would be sparser, more accurate, and almost free of artifacts. The validity of the methodology is verified with the analysis results of vibration signals measured from fault-injection experiment and industrial wind turbine transmission system. The comparisons highlight the advantages of the methodology over several common methods in suppressing artifacts and extracting the sparse transient vibration signatures of bearing structural damages.

A fully blind and adaptive filter method to solve the inverse problem in vibration-based gear damage detection

“…[2][3][4][5][6] Vibration contains a large amount of real-time running health information of rotating components and it is easy to be monitored continuously during machine operation. [7][8][9][10][11] When localized damage exists in a gear, one of the obvious changes in vibration is that the vibration will contain extra repetitive impulse components caused by the repeated impact between the damage point and the meshing gear tooth. In vibration-based gear health monitoring, however, the fault signatures are usually corrupted by unknown impulse response function (IRF) and interference components (such as background noise, gear meshing harmonic components, and electromagnetic impulses) in vibration measurements.…”