Fuzzy inference petri net modelling and state flow simulation for quality anomaly monitoring and diagnosis

Fuzzy Reasoning Petri Nets (FRPNs) are highly suitable for diagnosing quality anomalies in uncertain scenarios. However, the unique graphical representation and complex logical structure of FRPNs pose challenges for traditional Petri net simulation methods, limiting the ability to effectively model FRPNs and hinder dynamic analysis of anomaly generation and propagation through simulation. This paper introduces a Stateflow-based modelling and simulation approach for FRPNs, utilizing elements from the Stateflow diagram, including state, transition, state action, and transition labels. These elements are used to represent the position, directed arcs, and transition rules of FRPNs. By incorporating transition relationships and monitoring logic, a Stateflow based simulation model of FRPNs is constructed. This model takes control chart data as input, dynamically analyses the occurrence level of abnormal patterns in the control chart, providing a demonstration of the diagnostic process and ultimately delivering a diagnostic outcome.