Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results
This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are...
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Veröffentlicht in: | International journal of machine tools & manufacture 2009-06, Vol.49 (7), p.537-553 |
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creator | Zhu, Kunpeng Wong, Yoke San Hong, Geok Soon |
description | This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area. |
doi_str_mv | 10.1016/j.ijmachtools.2009.02.003 |
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Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.</description><subject>Applied sciences</subject><subject>Exact sciences and technology</subject><subject>Industrial metrology. Testing</subject><subject>Mechanical engineering. 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subjects | Applied sciences Exact sciences and technology Industrial metrology. Testing Mechanical engineering. Machine design Tool condition monitoring Wavelet |
title | Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results |
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