Bayesian-Driven Predictive Replacement Planning in Consideration of Spare Parts Ordering  
Author Ruoran Han


Co-Author(s) Xiaobing Ma; Li Yang


Abstract This paper proposes a Bayesian-driven joint optimization policy of predictive replacement and spare parts ordering, where crucial lifetime parameters are updated at inspections to support joint planning. The health evolution trajectory is modeled by a generalized-form Wiener process, whose crucial pattern parameters are estimated combining Bayesian inference and maximum likelihood estimation (MLE) algorithm. Furthermore, the real-time remaining lifetime is calculated upon each inspection to jointly update the optimal ordering time, predictive replacement time, through the setting of a dynamic reliability threshold to trigger subsequent replacement/ordering decisions. The feasibility and superiority of the proposed planning approach are verified through a practical case study on health management of train bearings.


Keywords replacement planning, inspection, planning, Bayesian, spare part ordering, cost decision-making, lifetime inference
    Article #:  RQD28-289

Proceedings of 28th ISSAT International Conference on Reliability & Quality in Design
August 3-5, 2023