Model-based high-frequency matched filter arcing diagnostic system based on principal component analysis (PCA) clustering

Glenn O. Allgood; Belle R. Upadhyaya

doi:10.1117/12.380597

30 March 2000 Model-based high-frequency matched filter arcing diagnostic system based on principal component analysis (PCA) clustering

Glenn O. Allgood, Belle R. Upadhyaya

Author Affiliations +

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380597
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Arcing in high-energy systems can have a detrimental effect on the operational performance, energy efficiency, life cycle and operating and support costs of a facility. In can occur in motors, switching networks, and transformers and can pose a serious threat to humans who operate or work around the systems. To reduce this risk and increase operational efficiency, it is necessary to develop a capability to diagnose single and multiple arcing events in order to provide an effective measure of system performance. This calculated parameter can then be used to provide an effective measure of system health as it relates to arcing and its deleterious effects. This paper details the development of a model-based matched filter for an antenna that recognizes single and/or multiple arcing events in a direct current motor and calculates a functional measure of activity and a confidence factor based on an estimate of how well the data fit the matched filter model parameters. A principal component analysis is then performed on the descriptive statistics calculated from the model's input data stream to develop cluster centers for classifying non- arcing and arching events that are invariant to system operation set point. This approach also has a deployment benefit in that the PCA decreases the computational load on the classifier system by reducing the order of the system.

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Glenn O. Allgood and Belle R. Upadhyaya "Model-based high-frequency matched filter arcing diagnostic system based on principal component analysis (PCA) clustering", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380597

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