Fault Diagnosis In Discrete Event And Hybrid Systems

Fault Diagnosis in Discrete-event and Hybrid Systems

A framework for on-line passive fault diagnosis in discrete-event systems is proposed. In this approach, the system and the diagnoser (the fault detection system) do not have to be initialized at the same time, and no information about the state or even the condition (failure status) of the system before the initiation of diagnosis is required. First, a state-based approach for fault diagnosis in finite-state automata is presented. The design of the fault detection system has, in the worst case, exponential time complexity. A model reduction scheme with Polynomial time complexity is introduced to reduce the computational Complexity of the design. Next the use of timing information to improve the accuracy of diagnosis is considered. Instead of directly extending the framework to timed discrete-event systems, an alternative approach is taken which leads to significant reduction in on-line computing requirements, and in many cases, in the size of the diagnoser at the expense of more off-line design calculations. The issue of diagnosability of failures in this framework is also studied and necessary and sufficient conditions for diagnosability are derived. In addition, the cases where the discrete-event models used in fault issue is whether the discrete-event model contains enough information about the system for the purpose of fault diagnosis. In this regard, two different notions of consistency between high-level (discrete-event) and low-level (hybrid) models are introduced. Sufficient conditions for consistency are derived and a semi-algorithmic method for constructing suitable high-level discrete-event models from low-level hybrid systems is developed.

Fault Diagnosis in Discrete-event and Hybrid Systems [microform]

Fault Diagnosis of Hybrid Dynamic and Complex Systems

Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/non-incremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems.