A dynamic reliability assessment method for multi-state manufacturing system by merging imprecise observational information

Accurate reliability assessment of advanced manufacturing systems is essential for ensuring production efficiency, reducing downtime, and enabling intelligent maintenance strategies. In practical industrial environments, state observations obtained from sensors or expert evaluations are often imprecise. Effectively utilizing this uncertain information can substantially improve the precision of reliability evaluations. Conventional methodologies often encounter limitations in addressing this challenge, as manufacturing systems are generally characterized by networked production line configurations rather than traditional serial or parallel structures. Moreover, the effective integration of imprecise observational data is essential for the continuous updating of system reliability. This study introduces a novel approach for dynamic reliability evaluation of a multi-state manufacturing system (MSMS), incorporating both rework mechanisms and buffer elements to enhance the accuracy and applicability of system reliability assessments. The MSMS model can effectively depict the gradual degradation processes and diverse performance levels of manufacturing systems, allowing for a more realistic and detailed representation of system behavior over time compared to traditional binary-state models. This study employs the multistate flow network (MFN) model to construct the MSMS reliability assessment framework from a network structure perspective. Dynamic Bayesian networks (DBNs) are developed to update the reliability function of an individual MSMS by incorporating evidential observational data. An illustrative case study on the reliability update of an aluminum alloy wheel production line is presented to demonstrate the proposed methodology. The case study results further confirm the effectiveness of the approach.