Mixture Model Approach for Acoustic Emission Control of Cylindrical Pressure Equipment

[+] Author and Article Information
Hani Hamdan

 UTC, HEUDIASYC, UMR CNRS 6599, BP 20529, 60205 Compiègne Cedex, France and  CETIM, BP 80067, 60304 Senlis Cedex, Francehani.hamdan@utc.fr

Gérard Govaert

 UTC, HEUDIASYC, UMR CNRS 6599, BP 20529, 60205 Compiègne Cedex, Francegerard.govaert@utc.fr

J. Pressure Vessel Technol 128(3), 479-483 (Sep 29, 2005) (5 pages) doi:10.1115/1.2222377 History: Received November 09, 2004; Revised September 29, 2005

In this paper, we present a new and original mixture model approach for acoustic emission (AE) data clustering. AE techniques have been used in a variety of applications in industrial plants. These techniques can provide the most sophisticated monitoring test and can generally be done with the plant/pressure equipment operating at several conditions. Since the AE clusters may present several constraints (different proportions, volumes, orientations, and shapes), we propose to base the AE cluster analysis on Gaussian mixture models, which will be, in such situations, a powerful approach. Furthermore, the diagonal Gaussian mixture model seems to be well adapted to the detection and monitoring of defect classes since the weldings of cylindrical pressure equipment are lengthened horizontally and vertically (cluster shapes lengthened along the axes). The EM (Expectation-Maximization) algorithm applied to a diagonal Gaussian mixture model provides a satisfactory solution but the real time constraints imposed in our problem make the application of this algorithm impossible if the number of points becomes too big. The solution that we propose is to use the CEM (Classification Expectation-Maximization) algorithm, which converges faster and generates comparable solutions in terms of resulting partition. The practical results on real data are very satisfactory from the experts point of view.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Acoustic emission data and weldings on the unfolded surface of the cylindrical pressure equipment.

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Figure 2

The AE process chain

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Figure 3

The AE measurement chain

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Figure 4

Partition (20 clusters) obtained by our approach applied to a real data set of 2601 acoustic emission events



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