As in-situ structural integrity monitoring disciplines mature, there is a growing need to process sensor/actuator data efficiently in real time. Although smaller, faster embedded processors will contribute to this, it is also important to develop straightforward, robust methods to reduce the overall computational burden for practical applications of interest. This paper addresses the use of equivalent circuit modeling techniques for inferring structure attributes monitored using impedance perturbation spectroscopy. In pioneering work about ten years ago significant progress was associated with the development of simple impedance models derived from the piezoelectric equations. Using mathematical modeling tools currently available from research in ultrasonics and impedance spectroscopy is expected to provide additional synergistic benefits. For purposes of structural health monitoring the objective is to use impedance spectroscopy data to infer the physical condition of structures to which small piezoelectric actuators are bonded. Features of interest include stiffness changes, mass loading, and damping or mechanical losses. Equivalent circuit models are typically simple enough to facilitate the development of practical analytical models of the actuator-structure interaction. This type of parametric structure model allows raw impedance/admittance data to be interpreted optimally using standard multiple, nonlinear regression analysis. One potential long-term outcome is the possibility of cataloging measured viscoelastic properties of the mechanical subsystems of interest as simple lists of attributes and their statistical uncertainties, whose evolution can be followed in time. Equivalent circuit models are well suited for addressing calibration and self-consistency issues such as temperature corrections, Poisson mode coupling, and distributed relaxation processes.
Whatever specific methods come to be preferred in the field of structural health/integrity monitoring, the associated raw data will eventually have to provide inputs for appropriate damage accumulation models and decision making protocols. The status of hardware under investigation eventually will be inferred from the evolution in time of the characteristics of this kind of functional figure of merit. Irrespective of the specific character of raw and processed data, it is desirable to develop simple, practical procedures to support damage accumulation modeling, status discrimination, and operational decision making in real time. This paper addresses these concerns and presents an auto-adaptive procedure developed to process data output from an array of many dozens of correlated sensors. These represent a full complement of information channels associated with typical structural health monitoring applications. What the algorithm does is learn in statistical terms the normal behavior patterns of the system, and against that backdrop, is configured to recognize and flag departures from expected behavior. This is accomplished using standard statistical methods, with certain proprietary enhancements employed to address issues of ill conditioning that may arise. Examples have been selected to illustrate how the procedure performs in practice. These are drawn from the fields of nondestructive testing, infrastructure management, and underwater acoustics. The demonstrations presented include the evaluation of historical electric power utilization data for a major facility, and a quantitative assessment of the performance benefits of net-centric, auto-adaptive computational procedures as a function of scale.