The timely and accurate diagnosis and prognosis of fault conditions in mechanical structures and civil infrastructure using real-time measurements can play a critical role in ensuring the safe and sustainable operation of these structures. This, however, is inherently difficult because structural degradations/faults usually have very subtle characteristic signature with infinitely many possible patterns/profiles, which is further compounded by various uncertainties. The existing techniques fall short in addressing these challenges. The overarching goal of this research is to create a new framework of fault diagnosis and prognosis enabled by physics-guided data. This framework is built upon the integration of computational intelligence with high-fidelity modeling/analysis and the adaptation of a highly promising, non-contact sensor-structure interaction mechanism. The new modeling framework will lead to useful diagnostic and prognostic tools in many areas such as aerospace, marine, transportation, infrastructure, energy and power. This project will contribute significantly to the workforce training by promoting the interdisciplinary research of computing, sensing, and statistical analysis, and by promoting the concepts of resilient and sustainable systems.

The research encompasses a series of inter-related components. High-fidelity multi-scale physical models capable of characterizing high-frequency dynamic responses of complex structural systems with high efficiency will be created. Data-driven calibration of the physic-guided model to address the model inadequacy and bias issues will be formulated and established. Fault diagnosis algorithm through compressed sensing technique based on the calibrated physics-guided model will be developed. Fault prognosis through statistically rigorous mixed effects models and multivariate Gaussian process models will be synthesized. Combined with the adaptive sensor-structure integration mechanism, collectively these contributions form a new framework that can lead to orders-of-magnitude enhancement in sensitivity and robustness of structural fault diagnosis and prognosis.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of Connecticut
United States
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