Operation Analysis and Fault Path Recognition of Complex Industrial Systems Based on Boolean Networks

In modern Industrial Production Processes(IPP), the increasing complexity poses challenges for operation analysis and fault path recognition. This study proposes a dynamic modeling approach using Boolean Networks (BNs) for IPP operation analysis, offering greater dynamism and interpretability compared to existing approaches. Furthermore, different abnormal events lead to the same fault. To bridge this gap, we propose using BNs attractor cycle and semi tensor product (STP) bijective technique to convert fault paths into data sequences. This enables precise identification of fault paths and impacts even when different root causes lead to similar failures. Subsequently, we provide strategies to mitigate fault impacts and use BNs nodes fault propagation topology graphs to prove the interpretability. The results show this method is effective in handling BNs complexity and fault path length. Note to Practitioners—Our work provides plant operators with a dynamic tool for operation analysis and fault path recognition through logical reasoning rather than static rule-based systems. By transforming intricate fault propagation patterns into visualizable data sequences, the method simplifies decision-making and reveals the root causes for maintenance teams. Additionally, formulated criteria for mitigating fault impacts can be applied directly in industrial environments. The proposed topology graphs provide a systematic and visual representation of fault paths, and application in the Preheater case study demonstrates a significant reduction in system complexity and fault path length.