Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage

[+] Author and Article Information
Yonghong Peng

Department of Computing, University of Bradford, Bradford, West Yorkshire BD7 1DP, UKy.h.peng@brad.ac.uk

J. Manuf. Sci. Eng 128(1), 154-166 (Sep 09, 2004) (13 pages) doi:10.1115/1.1948399 History: Received November 10, 2003; Revised September 09, 2004

Extensive research has been performed to investigate effective techniques, including advanced sensors and new monitoring methods, to develop reliable condition monitoring systems for industrial applications. One promising approach to develop effective monitoring methods is the application of time-frequency analysis techniques to extract the crucial characteristics of the sensor signals. This paper investigates the effectiveness of a new time-frequency analysis method based on Empirical Model Decomposition and Hilbert transform for analyzing the nonstationary cutting force signal of the machining process. The advantage of EMD is its ability to adaptively decompose an arbitrary complicated time series into a set of components, called intrinsic mode functions (IMFs), which has particular physical meaning. By decomposing the time series into IMFs, it is flexible to perform the Hilbert transform to calculate the instantaneous frequencies and to generate effective time-frequency distributions called Hilbert spectra. Two effective approaches have been proposed in this paper for the effective detection of tool breakage. One approach is to identify the tool breakage in the Hilbert spectrum, and the other is to detect the tool breakage by means of the energies of the characteristic IMFs associated with characteristic frequencies of the milling process. The effectiveness of the proposed methods has been demonstrated by considerable experimental results. Experimental results show that (1) the relative significance of the energies associated with the characteristic frequencies of milling process in the Hilbert spectra indicates effectively the occurrence of tool breakage; (2) the IMFs are able to adaptively separate the characteristic frequencies. When tool breakage occurs the energies of the associated characteristic IMFs change in opposite directions, which is different from the effect of changes of the cutting conditions e.g. the depth of cut and spindle speed. Consequently, the proposed approach is not only able to effectively capture the significant information reflecting the tool condition, but also reduces the sensitivity to the effect of various uncertainties, and thus has good potential for industrial applications.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 2

Experimental set-up

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Figure 3

Cutting force obtained in different tool conditions

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Figure 7

Hilbert spectra of normal and broken tools

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Figure 8

Three-dimensional Hilbert spectra

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Figure 9

Distribution of energies of IMFs

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Figure 10

Hilbert spectrum for the detection of tool breakage

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Figure 11

Tool breakage detection based on characteristic IMF components

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Figure 12

Milling process with increased depth of cut and tool breakage

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Figure 13

Milling process with decreased depth of cut and tool breakage

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Figure 14

Milling process with increased spindle speed and tool breakage

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Figure 15

Milling process with decreased spindle speed and tool breakage

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Figure 1

Two time series and their Hilbert spectra based on EMD

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Figure 4

Spectra under different tool conditions

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Figure 5

IMFs of cutting force signals

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Figure 6

Estimated zero-crossing rates and energies of IMFs



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