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

合作研究：利用物理引导的数据分析方法进行结构故障诊断和预测

• 批准号: 1824761
• 负责人: Shiyu Zhou
• 金额: $ 24.49万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824761&HistoricalAwards=false
• 关键词: Collaborative; Research; Structural; Fault; Diagnosis

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.

使用实时测量的机械结构和民用基础设施中断层条件的及时，准确的诊断和预后可以在确保这些结构的安全和可持续运行中起关键作用。 然而，这本质上是困难的，因为结构降解和断层通常具有非常微妙的特征签名，并具有无限的许多可能的模式和轮廓，这进一步由各种不确定性加剧。现有技术在应对这些挑战方面缺乏。 这项研究的总体目标是创建一个由物理学引导的数据实现的故障诊断和预后的新框架。 该框架建立在计算智能与高保真建模和分析以及适应高度有前途的非接触式传感器结构相互作用机制的基础上。新的建模框架将在许多领域（例如航空航天，海洋，运输，基础设施，能源和功率）中导致有用的诊断和预后工具。 该项目将通过促进计算，感应和统计分析的跨学科研究以及促进弹性和可持续系统的概念来为劳动力培训做出重大贡献。研究涵盖了一系列相互关联的组件。 将创建具有高效率的复杂结构系统的高频动态响应的高保真多尺度物理模型。 将制定和确定以物理指导模型来解决模型不足和偏见问题的数据驱动校准。将开发基于校准物理引导模型的压缩感测技术的故障诊断算法。 通过统计严格的混合效应模型和多元高斯工艺模型的断层预后将合成。 结合自适应传感器结构整合机制，这些贡献构成了一个新的框架，可以导致结构性故障诊断和预后的敏感性和鲁棒性提高刻度级，这反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的criter scriter scriter scriter scriter scriter scriter scritia criter criter criteria criteria criter criteria criter criteria criteria criteria criter criteria criteria均值得一提。

项目成果

Remaining useful life prediction based on degradation signals using monotonic B-splines with infinite support

• DOI: 10.1080/24725854.2019.1630868
• 发表时间: 2019-08
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Salman Jahani;R. Kontar;Shiyu Zhou;D. Veeramani

Multioutput Gaussian Process Modulated Poisson Processes for Event Prediction

• DOI: 10.1109/tr.2021.3088094
• 发表时间: 2020-11
• 期刊: IEEE Transactions on Reliability
• 影响因子: 5.9
• 作者: Salman Jahani;Shiyu Zhou;D. Veeramani;Jeff Schmidt

Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach

• DOI: 10.1109/tpami.2020.2987482
• 发表时间: 2019-01
• 期刊: IEEE Transactions on Pattern Analysis and Machine Intelligence
• 影响因子: 23.6
• 作者: R. Kontar;Garvesh Raskutti;Shiyu Zhou

A deep learning approach for predicting critical events using event logs

• DOI: 10.1002/qre.2853
• 发表时间: 2021-02
• 期刊: Quality and Reliability Engineering International
• 影响因子: 2.3
• 作者: Congfang Huang;Akash Deep;Shiyu Zhou;D. Veeramani

Stochastic prognostics under multiple time-varying environmental factors

• DOI: 10.1016/j.ress.2021.107877
• 发表时间: 2021
• 期刊: Reliab. Eng. Syst. Saf.
• 作者: Salman Jahani;Shiyu Zhou;D. Veeramani