Please use this identifier to cite or link to this item:
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
Issue Date: Jun-2009
Citation: 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.
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
ISSN: 08906955
DOI: 10.1016/j.ijmachtools.2009.02.003
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jun 1, 2023


checked on May 25, 2023

Page view(s)

checked on May 25, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.