Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many present-day systems combine physical and computational processes, and are best modeled as hybrid systems, where the dynamic behavior combines continuous evolution interspersed with discrete configuration changes. Due to the complexity of such modern engineering systems, formal methods are required for reliable and correct design, analysis, and implementation of hybrid system diagnosers.
This dissertation presents a systematic, model-based approach to event-based diagnosis of hybrid systems based on qualitative abstractions of deviations from nominal behavior. The primary contributions of this work center on (i) incorporating relative measurement orderings into fault isolation for continuous and hybrid systems, which describe predicted temporal orderings of measurement deviations, (ii) providing algorithms for event-based diagnosis of single and multiple faults, (iii) developing an integrated framework for diagnosis of parametric, sensor, and discrete, i.e., switching faults in hybrid systems, and (iv) developing and implementing an efficient event-based diagnosis framework for continuous and hybrid systems that enables automatic design of event-based diagnosers and establishes notions of diagnosability for continuous and hybrid systems.
The effectiveness of the approach is demonstrated on two practical systems. First, the single fault diagnosis method for continuous systems is applied in a distributed fashion to formations of mobile robots. The results include a formal diagnosability analysis, scalability results, and experiments performed on a formation of robots. Second, the approach developed for hybrid systems diagnosis is applied to the Advanced Diagnostics and Prognostics Testbed, which is a complex electrical distribution system for spacecraft and aircraft applications. The results focus on a subset of the testbed, and include a diagnosability analysis, experiments from the actual testbed, and detailed simulation experiments that examine the performance of the diagnosis algorithms for different fault magnitudes and noise levels.