An inspection-based replacement planning in consideration of state-driven imperfect inspections. (April 2023) - Wang, Jiantai
Highlights: We establish an imperfect inspection model for two-stage continuous degradation process and verify it via a proposed simulation algorithm. Multi-functional inspection tasks are scheduled to sufficiently reveal system health status. The impact of inspection error on the optimization of degradation control limits and periodical inspection interval are explored. The applicability of the proposed model is validated via a case study of high-speed rail bearing. Abstract: Inspection, as a fundamental component of asset management, is crucial to reveal underlying defects and report health evolution. Due to technical restrictions or human errors, inspections are often imperfect to miss the actual system state, posing challenges to the optimality of decision-makings. This study investigates an inspection-based replacement management strategy for two-phase continuous deteriorating systems subject to inspection errors. The non-steady evolution trajectory is captured by piecewise stochastic process with random state transition time. Inspections are equally spaced to reveal the underlying system state (both discrete and continuous), with a certain probability to miss the hidden defective state. Upon inspection, two levels of control limits are jointly scheduled to adjust execution frequencies of preventive replacement. The cost model is formulated and optimized, and the impact of imperfect inspection on system performance is assessed. The applicability of the proposed optimization model is validated through a case study from high-speed train bearings, which scales well in operational cost control, reducing about 39 and 4% cost compared with two conventional policies.