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Proceedings Paper

Robust evaluation of time series classification algorithms for structural health monitoring
Author(s): Dustin Y. Harvey; Keith Worden; Michael D. Todd
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Paper Abstract

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Paper Details

Date Published: 9 March 2014
PDF: 8 pages
Proc. SPIE 9064, Health Monitoring of Structural and Biological Systems 2014, 90640K (9 March 2014); doi: 10.1117/12.2044790
Show Author Affiliations
Dustin Y. Harvey, Univ. of California, San Diego (United States)
Keith Worden, The Univ. of Sheffield (United Kingdom)
Michael D. Todd, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 9064:
Health Monitoring of Structural and Biological Systems 2014
Tribikram Kundu, Editor(s)

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