This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that much of the technology discussed herein is applicable to powering any type of low-power embedded sensing system regardless of the application.

This paper presents the development and application of a miniaturized impedance sensor node for structural health monitoring (SHM). A large amount of research has been focused on utilizing the impedance method for structural health monitoring. The vast majority of this research, however, has required the use of expensive and bulky impedance analyzers that are not suitable for field deployment. In this study, we developed a wireless impedance sensor node equipped with a lowcost integrated circuit chip that can measure and record the electrical impedance of a piezoelectric transducer, a microcontroller that performs local computing, and a wireless telemetry module that transmits the structural information to a base station. The performance of this miniaturized and portable device has been compared to results obtained with a conventional impedance analyzer, and its effectiveness has been demonstrated in an experiment to detect loss of preload in a bolted joint. Furthermore, for the first time, we also consider the problem of wireless powering of such SHM sensor nodes, where we use radio-frequency wireless energy transmission to deliver electrical energy to power the sensor node. In this way, the sensor node does not have to rely on an on-board 3 power source, and the required energy can be wirelessly delivered as needed by human or a remotely controlled robotic device.

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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.

A novel feature extracted from a nonlinear time series is presented within the context of vibration-based damage detection in a system. An eight-degree-of-freedom spring-mass-damper 'structure' is considered with damage incurred by a stiffness degradation in one spring. The system is excited with a chaotic input, and by tuning the Lyapunov exponents of the chaotic excitation to the dominant eigenvalue of the structure the dimensionality of the entire system is effectively controlled. Both the input and output are viewed in state space as geometric objects, and the effect of the damage is shown to alter the geometric properties of the corresponding attractors at a local level, which may be captured in construction of the feature. The utility of the feature is compared with that of a number of modal-based features and shown to be superior in resolving capability and in robustness.

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About the cover: The figure in the upper left is an idealization of an actual test structure with loose internal parts that was subjected to an 18 Hz base input on a shake table. Los Alamos National Laboratory, an affirmative action/ equal opportunity employer, is operated by Los Alamos