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
Hauptverfasser: Zhu, Kunpeng, Wong, Yoke San, Hong, Geok Soon
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container_title International journal of machine tools & manufacture
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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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source Elsevier ScienceDirect Journals
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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