In intelligent manufacturing systems, the machine condition and the product quality data are conveniently available. Developing an effective maintenance strategy based on these two sources can significantly enhance cost-effectiveness and system reliability. However, existing studies have primarily focused on maintenance strategies based solely on either machine condition or product quality monitoring, while their joint monitoring has received limited attention. This paper proposes a novel strategy for monitoring manufacturing machines by integrating degradation and quality data, and designs a fixed-number variable inspection interval (VII) scheme to address the high cost of frequent sampling. First, the machine degradation process is modelled by a Wiener process, and the relationship between machine degradation and product quality is established. The variation of product quality is monitored using the S2 control chart, and a fixed-number VII scheme is designed. Second, a joint condition-based maintenance (JCBM) strategy is formulated, including the derivation of the expected time, cost, and availability for each maintenance scenario. Optimal maintenance plans are determined by minimizing the expected cost rate (ECR). Finally, a case study on scroll production in an air compressor system demonstrates the proposed strategy, showing that JCBM outperforms traditional strategies in both cost-effectiveness and monitoring performance.
Li et al. (Sat,) studied this question.