Dual-Pattern Diagnosability of Discrete Event Systems under Dynamic Observation

Dual-pattern diagnosis compels the system to act correctly between a faulty pattern and a subsequent critical pattern, thereby avoiding major losses. This capability makes it more practically relevant than traditional fault diagnosis. A polynomial algorithm for verifying the dual-pattern diagnosis of discrete event systems under dynamic observation is proposed to address the high cost and low efficiency of sensor deployment under static observation. Firstly, a formal definition of dual-pattern diagnosis under dynamic observation is presented. Pattern recognizers are then constructed to label faulty and critical patterns, respectively. Subsequently, a verifier automaton is built through automaton composition to verify dual-pattern diagnosability, from which a sufficient and necessary condition is derived. Theoretical analysis of the case study show that the proposed polynomial algorithm not only effectively verifies the dual-pattern diagnosability of discrete event systems, but also optimizes resource utilization efficiency while strengthening system diagnosability by dynamically adjusting sensor trigger settings. This approach provides an efficient and economical solution for dual-pattern diagnosis of complex systems.