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，这些子系统通过带限通信网络在彼此之间连续地交换信息。这种复杂的大规模系统容易发生故障，任何子系统组件中的单个故障都可能导致整个系统失效或发生故障。该项目的动机是对解决方案的需求日益增长，该解决方案将允许对此类复杂系统，特别是安全关键系统进行可靠的实时监控和监督。当前的故障检测和隔离技术依赖于集中式信息处理，该信息处理基于从整个网络的传感器收集数据。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，这种方法是不实际的。该研究将使用一种基于先进数学工具的新型分散式方法来制定解决方案框架，该框架将在此类大型网络中实现实时故障检测和隔离，并提供弹性，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。该项目不仅有研究生的教育计划，而且还有一个强大的外展计划，针对本科生和代表性不足的学生的多学科群体，让他们参与控制系统，通信和算法交叉的真实科学体验。本计画的主要研究目标为建立非线性随机大系统之分散式故障诊断演算法之分析与计算基础。分布式版本的粒子滤波方法将作为派生的诊断算法的基础。粒子滤波技术是一个非常合适的估计故障诊断，因为它避免了线性和高斯噪声的假设通常发现在当前的最先进的。目标单片过程的监测网络的互连诊断节点与本地处理和通信能力。诊断网络根据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，保持计算复杂性最低。该项目将建立一种新的分布式故障检测方法，利用现代嵌入式系统（如无线传感器网络和多核处理器）的分散式架构和计算能力。具体任务包括：推导出一种计算效率高的集中式故障敏感滤波器，消除了对一组估计器进行故障隔离的需要;建立一种分析方法，用于根据局部观测和信息交换获得关于系统健康状况的全局推断，目标是具有地理上稀疏的子组件的整体过程;和制定一个分布式FD方法，细分的监测任务，以低阶，可能互连，组件的目标是高维过程，不能容纳的中央配置。这项研究结合了以前不同的概念，从估计理论，容错和组合，提供一个强大的和连贯的框架弹性，可用和可靠的系统。

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.