Collaborative Research: Structural Fault Diagnosis and Prognosis Utilizing a Physics-guided Data Analytics Approach

使用实时测量对机械结构和民用基础设施中的故障状况进行及时准确的诊断和预测，可以在确保这些结构的安全和可持续运行方面发挥关键作用。 然而，这是固有的困难，因为结构退化和故障通常具有非常微妙的特征签名，具有无限多的可能模式和轮廓，这进一步被各种不确定性所复杂化。现有的技术不足以应对这些挑战。 本研究的总体目标是创建一个新的故障诊断和预测框架，由物理指导的数据。 该框架是建立在计算智能与高保真建模和分析的集成以及非常有前途的非接触式传感器-结构交互机制的适应基础上的。新的建模框架将在航空航天、海洋、交通、基础设施、能源和电力等许多领域产生有用的诊断和预测工具。 本项目将通过促进计算、传感和统计分析的跨学科研究，并通过推广弹性和可持续系统的概念，为劳动力培训做出重大贡献。该研究包括一系列相互关联的组成部分。 将创建能够高效表征复杂结构系统的高频动态响应的高保真多尺度物理模型。 将制定和建立物理指导模型的数据驱动校准，以解决模型不足和偏倚问题。基于标定后的物理模型，开发了基于压缩感知技术的故障诊断算法。 将综合利用统计上严格的混合效应模型和多元高斯过程模型进行故障预测。 结合自适应传感器-结构集成机制，这些贡献共同形成了一个新的框架，可以导致结构故障诊断和预测的灵敏度和鲁棒性的数量级增强。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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 and faults usually have very subtle characteristic signature with infinitely many possible patterns and 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 and 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.