Power signal separation in milling process based on wavelet transform and independent component analysis

被引：47

作者：

Shao, Hua ^{[2
]}

Shi, Xinhua ^{[2
]}

Li, Lin ^{[1
]}

机构：

[1] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA

[2] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | 2011年 / 51卷 / 09期

关键词：

Power signals; Blind source separation; Wavelet transform; Independent component analysis; Cutting process monitoring; BLIND SOURCE SEPARATION; FLUTE BREAKAGE DETECTION; TOOL-WEAR; NEURONAL ACTIVITIES; TIME-SERIES; MALFUNCTIONS; TETRODE; SURFACE; SENSOR; FORCE;

D O I：

10.1016/j.ijmachtools.2011.05.006

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Among many machining condition monitoring systems, a spindle motor power monitoring system is considered as one of the most popular systems for plant floor applications. However, in practice, power signals are mixed with many signal sources relevant to cutting tool, cutting conditions as well as components of a machine tool, which contaminate with each other in feature extraction processes and decrease the monitoring reliability. In this paper, modified blind sources separation (BSS) technique is used to separate those source signals in milling process. A single-channel BSS method based on wavelet transform and independent component analysis (ICA) is developed, and source signals related to a milling cutter and spindle are separated from a single-channel power signal. The experiments with different tool conditions illustrate that the separation strategy is robust and promising for cutting process monitoring. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：701 / 710

页数：10

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