12 October 2006 Multiresolution approach to identification of recurring signal patterns
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Proceedings Volume 6383, Wavelet Applications in Industrial Processing IV; 63830D (2006) https://doi.org/10.1117/12.685692
Event: Optics East 2006, 2006, Boston, Massachusetts, United States
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.
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Sagar V. Kamarthi, Ibrahim Zeid, Lakshmanan Subramaniam, "Multiresolution approach to identification of recurring signal patterns", Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830D (12 October 2006); doi: 10.1117/12.685692; https://doi.org/10.1117/12.685692
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