Prognosis-driven reliability analysis and replacement policy optimization for two-phase continuous degradation

Numerous industrial assets inevitably encounter defect emergence during actual operation, which significantly aggravates the degradation process. This study investigates a prognosis-driven, multi-threshold inspection and replacement model for two-phase continuous degradation. Inspections are equally space to reveal the health state as well as real-time degradation level. Three degrees of control limits, namely corrective limit, defective limit and normal limit are scheduled to manipulate replacement frequencies under separate system states. At an inspection, if the system is diagnosed as normal while the degradation exceeds the normal limit, preventive replacement is postponed according to: (a) residual lifetime expectation adjusted by a control coefficient, and (b) defect identification time, whichever occurs first. Otherwise if the system is found defective with degradation exceeding the defective limit, replacement is also postponed according to a stricter coefficient. The cost model is formulated, which seeks to minimize the long-run cost rate through the joint optimization of inspection interval, control limits and safety coefficients. The applicability of the proposed model is justified via a case study on high-speed rail bearings.