| The need for reliability and robustness in present day systems requires that they possess the capability for accommodating faults in the controlled plant through tight integration of online fault detection, isolation, and identification with the system control loop. This thesis presents a model-based approach to online fault detection, isolation, and identification in complex systems. The plant models for such systems are necessarily hybrid, i.e., their behavior evolution combines continuous and discrete changes. Such systems can be seen as operating in regions (modes) of continuous operation interspersed with discontinuous (discrete) changes that move the system from one region to another. Some naturally occurring systems are inherently hybrid, for example the cell-cycle control system in biology. Hybrid models can also be used to represent embedded systems in the avionics, automotive, and robotics domains.; Our model-based diagnosis approach exploits the analytical redundancy in general purpose structural and/or functional models of the system. Applying model-based diagnosis techniques to hybrid systems presents an interesting set of challenges that mostly revolve around interactions of the continuous and discrete components of the system. The tracking of the system behavior evolution has to be performed across modes of operation. This requires continuous tracking, identification of discrete changes, and updating the model and state after a discrete change. The fault isolation has to reason across modes of operation to identify hypotheses that can explain all deviant observations. This may involve rolling back in the mode space to generate hypotheses and then rolling forward in the mode space to catch-up to the current system mode of operation. For fault accommodation, the quantitative value of the fault has to be determined so that appropriate corrective action may be taken. We present an integrated diagnosis architecture that tracks the hybrid system, and detects, isolated and identifies the fault. We use hybrid bond graphs as a comprehensive modeling framework from which models for the individual components of our diagnosis architecture are derived. We use these models to develop diagnosis algorithms that combine hybrid behavior tracking with mode detection and combined qualitative-quantitative reasoning techniques in the continuous domain. |
