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，其中有许多空间分布的子系统，这些子系统通过有限带宽的通信网络不断地相互交换信息。如此复杂的大型系统容易发生故障，任何子系统部件的单个故障都可能导致整个系统失效或故障。这个项目的动机是越来越需要一种解决方案，能够对这些复杂的系统，特别是安全关键系统进行可靠的实时监控和监督。目前的故障检测与隔离技术依赖于集中的信息处理，基于从整个网络中采集传感器的数据。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，这种方法不实用。该研究将使用一种基于先进数学工具的新颖分散方法来制定解决方案框架，该框架将在如此大的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、污染源定位以及医疗监测。该项目不仅为研究生制定了教育计划，而且还为本科生和代表性不足的学生提供了强有力的外展计划，使他们参与到控制系统、通信和算法交叉领域的真实科学体验中。本课题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分析和计算基础。粒子滤波方法的分布式版本将作为衍生诊断算法的基础。粒子滤波技术是一种非常适合于故障诊断的估计方法，因为它避免了当前技术中常见的线性和高斯噪声假设。目标单片进程由具有本地处理和通信能力的互连诊断节点网络监控。诊断网络通过局部观测和相邻节点之间的局部信息交换，推断出整个系统的信息。该方法同时进行实时故障检测和隔离，使计算复杂度降到最低。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）的分散架构和计算能力。具体任务包括：推导一种计算效率高、集中的故障敏感滤波器，消除了对一组估计器进行故障隔离的需要；建立了一种基于局部观测和信息交换获得系统健康全局推断的分析方法，目标是具有地理稀疏子组件的整体过程；以及分布式FD方法的制定，该方法将监控任务细分为针对高维过程的低阶（可能相互关联的）组件，这些高维过程无法被中心配置所容纳。这项研究结合了先前来自估计理论、容错和组合学的不同概念，为弹性、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.