Distributed Fault Diagnosis for Large-Scale Nonlinear Stochastic Systems

某些工业和商业过程或系统由越来越大和复杂的网络组成，该网络具有多个空间分布的子系统，这些子系统在有限频带的通信网络上持续地彼此交换信息。这种复杂的大系统很容易发生故障，任何一个子系统组件的单一故障都可能导致整个系统发生故障或故障。该项目的动机是对解决方案的需求日益增长，以便能够可靠地实时监测和监督这类复杂系统，特别是安全关键系统。当前的故障检测和隔离技术依赖于基于从整个网络上的传感器收集数据的集中式信息处理。对于复杂和大规模的网络，由于计算复杂性和通信带宽的限制，这种方法并不实用。这项研究将使用基于先进数学工具的新型分散方法来制定解决方案框架，该框架将在如此大型的网络中实现实时故障检测和隔离，并将提供弹性、可用性和可靠性。项目成果将影响更广泛的社会应用，如网络安全、多机器人系统、结构和农业监测、污染源定位和医疗监测。该项目不仅有一个面向研究生的教育计划，还有一个强大的外展计划，针对本科生和代表性不足的多学科学生群体，让他们参与到控制系统、通信和算法的交叉领域的真实世界科学体验中。本项目的主要研究目标是为非线性、大规模随机系统的分布式故障诊断算法建立一个分析和计算基础。粒子滤波方法的分布式版本将作为派生的诊断算法的基础。粒子滤波技术是一种非常适合故障诊断的估计器，因为它避免了目前最先进的技术中常见的线性和高斯噪声假设。目标单片过程由具有本地处理和通信能力的互连诊断节点网络监控。诊断网络基于局部观测和相邻节点之间的局部信息交换来推断整个系统的信息。该方法同时进行实时故障检测和隔离，将计算复杂度保持在最低水平。该项目将建立一种新的分布式故障检测方法，该方法利用无线传感器网络和多核处理器等现代嵌入式系统的分散体系结构和计算能力。具体任务包括：推导一种计算效率高的集中式故障敏感过滤器，该过滤器不需要一组估计器进行故障隔离；建立一种分析方法，用于根据局部观察和信息交换获得关于系统健康的全局推断，以具有地理稀疏子组件的整体过程为目标；以及制定分布式FD方法，该方法将监测任务细分为低阶的、可能相互关联的、针对不能由中央配置容纳的高维过程的组件。这项研究结合了估计理论、容错和组合学中以前不同的概念，为具有弹性、可用和可靠的系统提供了一个健壮和连贯的框架。

Certain industrial and commercial processes or systems consist of increasingly large and complex networks with a number of spatially distributed sub-systems that continually exchange information between each other over a band-limited communication network. Such complex large scale systems are vulnerable for faults and a single malfunction in any sub-system component can cause the entire system to fail or malfunction. This project is motivated by a growing need for a solution that will allow reliable real-time monitoring and supervision of such complex systems especially the safety-critical systems in particular. Current fault detection and isolation technology relies on centralized information processing based on collecting data from the sensors from across the entire network. For complex and large scale networks this approach is not practical due to computational complexity and communication bandwidth limitations. The research will use a novel decentralized approach based on advanced mathematical tools to formulate a solution framework that will enable real-time fault detection and isolation in such large networks and will provide resiliency, availability, and dependability. The project outcomes will impact broader societal applications such as cyber-security, multi-robot systems, structural and agricultural monitoring, pollution source localization, and healthcare monitoring. The project not only has an educational plan for graduate students but also a strong outreach program aimed at multidisciplinary groups of undergraduate and under-represented students to engage them in a real-world scientific experience that lies in the intersection of control systems, communications and algorithms. The primary research objective of this project is to establish an analytical and computational foundation for distributed fault diagnosis algorithms of nonlinear, large-scale stochastic systems. A distributed version of the particle filtering method will serve as the foundation of the derived diagnostic algorithms. The particle filtering technique is a highly suitable estimator for fault diagnosis since it avoids linearity and Gaussian noise assumptions typically found in current state-of-the-art. The target monolithic process is monitored by a network of interconnected diagnostic nodes with local processing and communication capabilities. The diagnostic network infers information of the entire system based on partial observations and local information exchange between neighboring nodes. The methodology conducts simultaneous real-time fault detection and isolation, keeping the computational complexity to a minimum. The project will establish a novel distributed fault detection methodology that takes advantage of the decentralized architecture and computational strength of modern embedded systems such as wireless sensor networks and multi-core processors. The specific tasks include: derivation of a computationally efficient, centralized fault-sensitive filter that eliminates the need for a bank of estimators to conduct fault isolation; establishment of an analytical method for obtaining global inference about the health of the system based on local observations and information exchange, targeting monolithic processes with geographically sparse subcomponents; and formulation of a distributed FD method that subdivides the monitoring task to low-order, possibly interconnected, components targeted to high-dimensional processes that cannot be accommodated by a central configuration. This research combines previously disparate concepts from estimation theory, fault-tolerance and combinatorics to provide a robust and coherent framework for resilient, available and dependable systems.