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Proceedings Paper

Multiresolution approach to identification of recurring signal patterns
Author(s): Sagar V. Kamarthi; Ibrahim Zeid; Lakshmanan Subramaniam
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Paper Abstract

Manufacturing processes are generally monitored by observing uniformly sampled process signals collected from application specific sensors. Effective process monitoring and control requires identification of different types of variations, including recurring patterns, in process variables. From the process control view point, any repeating patterns in the process measurements will warrant an investigation into potentially assignable causes. In order to devise an effective process control scheme, a novel method for identifying the repeated occurrence of patterns in process measurements is described in this paper. First the sampled process signal is decomposed into signals of different resolution using a wavelet transform. Next, a frequency index is assigned to every sampling point of the process signal at every resolution level to improve the pattern recognition. Recurring patterns are first detected at different resolutions and are then integrated to arrive at the final results. The experimental results show that the method used in this work accurately detects a broader family of recurring patterns even in the presence of noise.

Paper Details

Date Published: 12 October 2006
PDF: 7 pages
Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830D (12 October 2006); doi: 10.1117/12.685692
Show Author Affiliations
Sagar V. Kamarthi, Northeastern Univ. (United States)
Ibrahim Zeid, Northeastern Univ. (United States)
Lakshmanan Subramaniam, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 6383:
Wavelet Applications in Industrial Processing IV
Frédéric Truchetet; Olivier Laligant, Editor(s)

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