Optimal Damage Detection and Prognosis Vis Elastic Stress Wave Scattering

Professor Michael Todd

Ultrasonic guided wave interrogation using both coherent-phase arrays and sparse arrays (sparsity defined as arrays whose average sensor-to-sensor distance is significantly longer than the interrogating wavelengths) has evolved into a very active research area. This research focuses on the detection, classification, and prognosis of damage using elastic waves as the interrogation mechanism.


The novel approach in this work is the embedding of stochastic models to account for uncertainty of model/physical parameters, in order to derive an optimal detection process that supports predictive modeling with quantified uncertainty. Research is focusing on maximum likelihood estimates for detecting and localizing small scatterers (holes, asymmetric cracks) in metallic plate-like structures. Detection is accomplished using generalized likelihood ratio test (GLRT) and Bayesian detectors in conjunction with a broadband beamformer to estimate the arrival angle of scattered waveforms.