A Robust Framework for Fault Diagnosis in Discrete Event Systems

Abstract

The reliability and safety of modern industrial applications demand efficient fault detection and isolation (FDI) systems. Unfortunately, traditional diagnostic techniques are often highly sensitive to measurement noise, leading to frequent false alarms. Additionally, many contemporary methods require exhaustive training datasets, limiting their practical applicability. To address these challenges, this paper proposes a novel hybrid diagnostic framework applied to a continuous fluid mixing and filling process. The presented framework combines continuous residual analysis with discrete-event temporal reasoning. A timed diagnoser, implemented using Stateflow, represents bounded time intervals as active behavioral guards to continuously monitor the system against nominal timelines. When a temporal violation is detected, a Fuzzy Inference System (FIS) is triggered to isolate the root cause. The FIS effectively resolves diagnostic ambiguities—particularly masking effects caused by concurrent actuator and sensor malfunctions by correlating continuous fluid levels with discrete temporal deviations. Extensive simulations under noisy conditions demonstrate that the proposed hybrid architecture achieves a 100% detection rate and a 99.0% fault localization accuracy, while completely eliminating false alarms (0% false alarm rate). These quantitative results significantly outperform classical observer-based techniques and standard discrete Petri net methodologies, confirming the robustness and superiority of the proposed framework.