Fault diagnosis of timed discrete event systems

The project provides tools and modeling advice for diagnosing faults in partially observable timed discrete‑event systems (DES) that reset a single clock at every event. In industrial electrical equipment and automated machines, it is important to model faults as timed transitions inside a discrete‑event framework so that diagnostics can be accurate. The approach builds a **zone automaton** that converts the continuous‑time constraints of the DES into a purely discrete‑event model. This zone automaton is then combined with a fault monitor using parallel composition, creating a full‑scale fault recognizer. With this setup, we can calculate the exact diagnosis state for any timed observation sequence—i.e., a list of observed events together with

their timing intervals. The research gives a clear procedure for checking whether a complex system can be diagnosed under time constraints, helping engineers design reliable diagnostic tools for electrical machines and automated drives. Simulations show that including timing information cuts down the uncertainty in locating faults compared to methods that ignore time.

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