Hidden Markov model based classification of structural damage

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.