Fault Detection and Diagnosis for Uncertain Nonlinear Systems Using Set-Based State Estimation

该项目将通过推进自动化方法来检测和诊断复杂工程过程中的部件故障和其他异常事件（如故障或故障），为国家的经济繁荣和安全做出贡献。美国许多关键行业越来越依赖复杂、集成和高度自动化的系统，包括制造业、化工、能源、运输甚至医药。这些系统的故障是不可避免的，而那些未被发现或误诊的系统可能会造成巨大的经济损失，灾难性的环境破坏和生命损失。该项目将开发检测故障发生和确定其根本原因的方法，大大提高速度和准确性，从而能够迅速采取纠正行动。该项目的教育部分将包括本科生课程开发和纳入本科生研究人员在南欣森冷水机组在克莱姆森大学实施的开发方法。该项目的研究目标是开发和实验验证先进的基于集合的故障检测和诊断（FDD）算法，大大提高了检测速度和精度的不确定性非线性系统，通过结合国家的最先进的集为基础的FDD算法的线性系统先前开发的主要研究者与一个强大的新的方法，约束下的不确定性的非线性系统的可达集。当前的工业故障检测系统通常通过将过程测量与历史数据进行比较来操作。因此，它们区分故障和正常干扰以及确定根本原因的能力，关键取决于历史数据对当前条件的适用性。因此，这些方法在面对频繁的瞬态、高度可变或不确定的输入、非线性动力学或首次故障时容易出现误报和误诊。所开发的方法将利用过程模型来表征与正常操作一致的过程输出，并为故障检测和诊断提供严格的基础。该项目特别考虑使用基于集合的状态估计器来提供这些输出的有保证的外壳的方法，这消除了错误警报的可能性。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will contribute to the economic prosperity and safety of the nation by advancing automated methods for detecting and diagnosing component malfunctions and other abnormal events (such as, faults or failures) in complex engineering processes. Many key US industries are increasingly reliant on complex, integrated, and highly automated systems, including manufacturing, chemicals, energy, transportation, and even medicine. Failures in such systems are inevitable, and those that go undetected or misdiagnosed can be responsible for massive economic losses, catastrophic environmental damage, and loss of life. This project will develop methods for detecting the occurrence of faults and determining their root causes with significantly increased speed and accuracy, thus enabling rapid corrective actions. The educational component of the project will include undergraduate curriculum development and inclusion of undergraduate researchers in implementation of the developed methods on the South Hinson Chiller plant at Clemson University.The research objective of this project is to develop and experimentally validate advanced set-based fault detection and diagnosis (FDD) algorithms with greatly enhanced detection speed and accuracy for uncertain nonlinear systems, by combining state-of-the-art set-based FDD algorithms for linear systems previously developed by the principal investigator with a powerful new approach for bounding the reachable sets of nonlinear systems under uncertainty. Current industrial fault detection systems most often operate by comparing process measurements to historical data. Consequently, their ability to distinguish faults from normal disturbances, and to identify root causes, depends critically on the applicability of the historical data to the current conditions. Thus, these methods are susceptible to false alarms and misdiagnoses when confronted with frequent transients, highly variable or uncertain inputs, nonlinear dynamics, or first-of-a-kind faults. The developed methods will exploit process models to characterize the process outputs consistent with normal operation and to provide a rigorous basis for fault detection and diagnosis. This project specifically considers methods that furnish a guaranteed enclosure of these outputs using set-based state estimators, which eliminates the possibility of false alarms.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.