Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

被引：307

作者：

Zhu Kunpeng ^{[1
]}

San, Wong Yoke ^{[1
]}

Soon, Hong Geok ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Mech Engn, Singapore 119260, Singapore

来源：

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | 2009年 / 49卷 / 7-8期

关键词：

Wavelet; Tool condition monitoring; TIME-FREQUENCY-ANALYSIS; IN-PROCESS DETECTION; DRILL BIT BREAKAGE; ACOUSTIC-EMISSION; FEATURE-EXTRACTION; NEURAL-NETWORKS; FAULT-DETECTION; FLANK WEAR; SINGULARITY DETECTION; TURNING OPERATIONS;

D O I：

10.1016/j.ijmachtools.2009.02.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：537 / 553

页数：17

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