Abstract
The ability to detect and classify damages in complex materials and structures is an important problem from both safety and economical perspectives. This paper develops a novel approach based on Hidden Markov Models (HMMs) for the classification of structural damage. Our approach here is based on using HMMs for modeling the time-frequency features extracted from time-varying structural data. Unlike conventional deterministic methods, the HMM is a stochastic approach which better accounts for the uncertainties encountered in the structural problem and leads to a more robust health monitoring system. The utility of the proposed approach is demonstrated via example results for the classification of fastener damage in an aluminum plate.
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