A Human‐Centric Based Network Model for Quality Risk Identification and Control in Ship Propulsion System Assembly Under Industry 5.0

ABSTRACT Industry 5.0 is catalyzing a paradigm shift toward human‐centric intelligent manufacturing, where the operational, cognitive, and social attributes of individuals critically influence production quality and resilience. Traditional quality control methods, however, fall short in dynamically identifying and mitigating human‐factor‐induced risks within complex human‐physical systems. To address this gap, this study proposes a proactive quality risk identification and control framework for ship propulsion system assembly, leveraging real‐time Industrial Internet of Things (IIoT) data. The framework begins by establishing a comprehensive human factors risk point system, systematically mapping human roles such as operator, decision‐maker, and social being to 24 specific process risk points. By integrating Reverse Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA), real‐time fault logs are decomposed into meta‐faults. Weighted Association Rule Mining (WARM) is then employed to construct a dual‐layer correlation network between meta‐faults and risk points, revealing hierarchical causal relationships and enabling precise, dynamic risk prioritization. Based on this, a human‐machine collaborative risk management strategy is proposed. This research integrates human factors into digital quality management, providing an extensible methodology for proactive quality management aligned with Industry 5.0's human‐centered objectives.