SUMMARYIn this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider non-linear system input=output relationships are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classiÿer. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to non-linear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the vibration-based method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based active sensing method using piezoelectric (PZT) material as both an actuator and a sensor is then investigated as an alternative solution to the problem of damage localization.

The Operationally Responsive Space (ORS) strategy hinges, in part, on realizing technologies which can facilitate the rapid deployment of satellites. Presently, preflight qualification testing and vehicle integration processes are time consumptive and pose as two significant hurdles which must be overcome to effectively enhance US space asset deployment responsiveness. There is a growing demand for innovative embedded Structural Health Monitoring (SHM) technologies which can be seamlessly incorporated onto payload hardware and function in parallel with satellite construction to mitigate lengthy preflight checkout procedures. In this effort our work is focused on the development of a joint connectivity monitoring algorithm which can detect, locate, and assess preload in bolted joint assemblies. Our technology leverages inexpensive, lightweight, flexible thin-film macrofiber composite (MFC) sensor/actuators with a novel online, data-driven signal processing algorithm. This algorithm inherently relies upon Chaotic Guided Ultrasonic Waves (CGUW) and a novel cross-prediction error classification technique. The efficacy of the monitoring algorithm is evaluated through a series of numerical simulations and experimentally in two test configurations. We conclude with a discussion surrounding further development of this approach into a commercial product as a real-time flight readiness indicator.

In this study, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is explored. Damage sensitive features that explicitly consider the nonlinear system input/output relationships produced by damage are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS is useful in this case because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the current method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based method using piezoelectric (PZT) material as both an actuator and a sensor is then proposed as a possible solution to the problem of damage localization.

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