Abstract
The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.
Highlights
Safety of a mechanical system is very important for industry
An effective wavelet-domain shrinkage processor that makes use of quantum theory to analyze the interscale dependencies of wavelet subbands is proposed for mechanical vibration signals
The first is that an adaptive nonGaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients with a tunable parameter is designed, and a better practical applicability is achieved
Summary
Safety of a mechanical system is very important for industry. The study of fault feature detection in machinery has received considerable attentions during the past decades. The most popular tool is vibration-based analysis. For the studies about practical mechanical vibration signals, noise is an inevitable factor in the measured signals which always inhibits the extraction of true signal signatures for diagnosis. Noise depressing in mechanical time series is an important issue for accurate fault diagnosis. As an effective analysis technique, wavelet transform is a frequently used tool for nonstationary signal processing in many fields. The most popular methods are VisuShrink, SureShrink, BayesShrink, and NeighShrink. Several shrinkage functions have been modified for better noise reduction based on the above shrinkage approaches [1,2,3,4,5,6,7,8]
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