Integrating Time Interval Petri Nets with fuzzy logic for enhanced failure monitoring and detection

This research introduces a novel approach to fault detection and diagnosis by integrating Fuzzy Logic with Time Interval Petri Nets (TIPNs) to generate Fuzzy Interval Petri Nets (FIPNs). The method offers a robust and adaptable solution for detecting and diagnosing faults in industrial systems, addressing both temporal and non-temporal constraints while managing uncertainty effectively. This is achieved by combining the uncertainty-handling strengths of fuzzy logic with the time modeling capabilities of TIPNs. For the validation of the designed framework, a practical case study of an industrial fluid mixing system has been deployed. The results demonstrate the effectiveness of the suggested FIPN model in preserving system performance and adaptability even in the presence of operational disturbances. Additionally, the results demonstrate an improvement in fault detection accuracy and system robustness.