Condition-based monitoring for a serial manufacturing system considering degradation and quality variation simultaneously
针对串行多阶段制造系统，提出一种联合监测方案，同时考虑质量相关部件退化与关键产品特性变异，通过蒙特卡洛仿真和实例验证其有效性。
A serial multistage manufacturing system (SMMS), consisting of multiple consecutive stations, is commonly used in manufacturing to improve production efficiency. In an intelligent manufacturing system, the degradation data of quality-related component (QRC) and the key product characteristic (KPC) data at each stage are conveniently available. Monitoring both QRC degradation and KPC quality is essential for ensuring the reliability of SMMS and the consistency of final products. Thus, this paper proposes a joint monitoring (JM) scheme that considers the interaction between QRC degradation and KPC in SMMS. First, the interaction considering the influence of non-conforming items is modelled. Second, a multivariate directional generalised likelihood ratio (MDGLR) chart is used to monitor the variation of KPC at each stage, and the hazards rate of QRC is assessed through a proportional hazards model (PHM). When the JM scheme triggers an alarm, a diagnostic approach is proposed to identify the stage that requires maintenance. Subsequently, Monte Carlo simulation is conducted to compare the JM scheme with existing schemes, demonstrating its superiority in terms of average time to signal (ATS). Finally, a case study on the shaft sleeve production process in a four-stage manufacturing system validates the practical applicability of the proposed scheme.