Intelligent monitoring and fault tolerance in large-scale distributed systems

Abstract

Electronic devices are starting to become widely available for monitoring and controlling large-scale distributed systems. These devices may include sensing capabilities for online measurement, actuators for controlling certain variables, microprocessors for processing information and making realtime decisions based on designed algorithms, and telecommunication units for exchanging information with other electronic devices or possibly with human operators. A collection of such devices may be referred to as a networked intelligent agent system. Such systems have the capability to generate a huge volume of spatial-temporal data that can be used for monitoring and control applications of large-scale distributed systems. One of the most important research challenges in the years ahead is the development of information processing methodologies that can be used to extract meaning and knowledge out of the ever-increasing electronic information that will become available. Even more important is the capability to utilize the information that is being produced to design software and devices that operate seamlessly, autonomously and reliably in some intelligent manner. The ultimate objective is to design networked intelligent agent systems that can make appropriate real-time decisions in the management of large-scale distributed systems, while also providing useful high-level information to human operators. One of the most important classes of large-scale distributed systems deals with the reliable operation and intelligent management of critical infrastructures, such as electric power systems, telecommunication networks, water systems, and transportation systems. The design, control and fault monitoring of critical infrastructure systems is becoming increasingly more challenging as their size, complexity and interactions are steadily growing. Moreover, these critical infrastructures are susceptible to natural disasters, frequent failures, as well as malicious attacks. There is a need to develop a common system-theoretic fault diagnostic framework for critical infrastructure systems and to design architectures and algorithms for intelligent monitoring, control and security of such systems. The goal of this presentation is to motivate the need for health monitoring, fault diagnosis and security of critical infrastructure systems and to provide a fault diagnosis methodology for detecting, isolating and accommodating both abrupt and incipient faults in a class of complex nonlinear dynamic systems. A detection and approximation estimator based on computational intelligence techniques is used for online health monitoring. Various adaptive approximation techniques and learning algorithms will be presented and illustrated, and directions for future research will be discussed.