Toward an equation-oriented framework for diagnosis of complex systems

Diagnosis of complex systems is a critical area for most real-world systems. Given the wide range of system types, including physical systems, logic circuits, state-machines, control systems, and software, there is no commonly-accepted modeling language or inference algorithms for model-Based Diagnosis (MBD) of such systems. Designing a language that can be used for modeling such a wide class of systems, while being able to efficiently solve the model, is a formidable task. The computational efficiency with which a given model can be solved, although often neglected by designers of modeling languages, is a key to parameter identification and answering MBD challenges. We address this freedom-of-modeling versus model-solving efficiency trade-off challenge by evolving a language for MBD of physical system, called LYDIA. In this paper we report on the abilities of LYDIA to model a class of physical systems, the algorithms that we use for solving MBD problems and the results that we have obtained for several challenging systems.