Condition-based maintenance for serial multistage manufacturing system considering reliability and quality over a finite horizon

Abstract Serial multistage manufacturing systems (SMMS), comprising multiple consecutive stages, are widely adopted in modern industry. At each stage, quality-related components (QRCs) refer to machine parts that directly impact product quality, while key product characteristics (KPCs) reflect both product performance and customer requirements. The quality of KPCs serves as an indicator of both product quality and machine condition, whereas the condition of QRCs reflects component health and provides early warnings of potential quality issues. Simultaneously monitoring both KPCs and QRCs across all stages is vital for ensuring system reliability and maintaining consistent product quality. To best of our knowledge, in SMMS, existing studies have primarily focused on the economic design of condition-based maintenance (CBM) strategies for either monitoring QRCs or KPCs individually, while their joint monitoring has received limited attention. To address this gap, this study proposes a cost-effective joint CBM strategy for SMMS over a finite horizon. A multivariate generalized likelihood ratio (MGLR) chart is employed to monitor the variations in KPCs, and the hazard rate of QRCs is evaluated using a proportional hazards model (PHM). Alarm scenarios and cost formulations are developed over a finite horizon, and a simulation framework is established to calculate the expected total cost (ETC). Subsequently, a simulation-based genetic algorithm is employed to minimize the ETC. Finally, the proposed strategy is validated on the four-stage scroll machining process in a compressor manufacturing system, demonstrating its effectiveness.