Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ijmachtools.2009.02.003
Title: Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results
Authors: Zhu, K.P. 
Wong, Y.S. 
Hong, G.S. 
Keywords: Tool condition monitoring
Wavelet
Issue Date: Jun-2009
Source: Zhu, K.P., Wong, Y.S., Hong, G.S. (2009-06). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools and Manufacture 49 (7-8) : 537-553. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ijmachtools.2009.02.003
Abstract: 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. © 2009 Elsevier Ltd. All rights reserved.
Source Title: International Journal of Machine Tools and Manufacture
URI: http://scholarbank.nus.edu.sg/handle/10635/68462
ISSN: 08906955
DOI: 10.1016/j.ijmachtools.2009.02.003
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