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This paper describes the formulation of a maximum-likelihood estimate of damage location for guided-wave structural health monitoring (GWSHM) using a minimally informed, Rayleigh-based statistical model of scattered wave measurements. Also introduced are two statistics-based methods for evaluating localization performance: the localization probability density function estimate and the localizer operating characteristic curve. Using an ensemble of measurements from an instrumented plate with stiffening stringers, the statistical performance of the so-called Rayleigh maximumlikelihood estimate (RMLE) is compared with that of seven previously reported localization methods. The RMLE proves superior in all test cases, and is particularly effective in localizing damage using very sparse arrays consisting of as few as three transducers. The probabilistic basis used for modelling the complicated wave scattering behaviour makes the algorithm especially suited for localizing damage in complicated structures, with the potential for improved performance with increasing structure complexity.

We propose a novel approach for optimal actuator and sensor placement for active sensing-based structural health monitoring. Of particular interest is the optimization of actuator-sensor arrays making use of ultrasonic wave propagation for detecting damage in thin plate-like structures. Using a detection theory framework, we establish the optimum configuration as the one that minimizes Bayes risk. The detector incorporates a statistical model of the active sensing process that accounts for both reflection and attenuation features, implements pulse-echo and pitch-catch actuation schemes, and takes into account line-of-site. The optimization space was searched using a genetic algorithm with a time-varying mutation rate. For verification, we densely instrumented a concave-shaped plate and applied artificial, reversible damage to a large number of randomly generated locations, acquiring active sensing data for each location. We then used the algorithm to predict optimal subsets of the dense array. The predicted optimal arrangements proved to be among the top performers when compared to large sets of randomly generated arrangements.

A new wireless sensing network paradigm is presented for structural monitoring applications. In this approach, both power and data interrogation commands are conveyed via a mobile agent that is sent to sensor nodes to perform intended interrogations, which can alleviate several limitations of the traditional sensing networks. Furthermore, the mobile agent provides computational power to make near real-time assessments on the structural conditions. This paper will discuss such prototype systems, which are used to interrogate impedance-based sensors for structural health monitoring applications. Our wireless sensor node is specifically designed to accept various energy sources, including wireless energy transmission, and to be wirelessly triggered on an as-needed basis by the mobile agent or other sensor nodes. The capabilities of this proposed sensing network paradigm are demonstrated in the laboratory and the field.