Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

• Published in: Mechanical systems and signal processing Vol. 24; no. 1; pp. 119 - 137
• Main Authors: Wang, Yanxue, He, Zhengjia, Zi, Yanyang
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 2010; Elsevier
• Subjects: Applied sciences; Bearings, bushings, rolling bearings; Compound faults; Drives; Dual-tree complex wavelet transform; Exact sciences and technology; Feature extraction; Fracture mechanics (crack, fatigue, damage...); Fundamental areas of phenomenology (including applications); Health monitoring; Information, signal and communications theory; Mechanical engineering. Machine design; Physics; Rotating machinery; Signal and communications theory; Signal denoising; Solid mechanics; Structural and continuum mechanics; Telecommunications and information theory; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Health monitoring; Feature extraction; Compound faults; Rotating machinery; Signal denoising; Dual-tree complex wavelet transform; Measurement; Empirical mode decomposition; Noise reduction; Duality; Vibration test; Modeling; Gear; Rolling bearing; Hilbert transformation; Excavations; Restoration; Tree; Robustness; Pattern extraction; Monitoring; Vibration; Rotating machine; Dual transformation; Shrinkage; Shift invariance; Wavelet transformation; Aliasing; Signal processing; Fault diagnostic; Defect detection; Crack; Signal to noise ratio
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2009.06.015

In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm.