Maintenance optimization for rolling bearing based on delay time model

Rolling bearings are critical components in rotating machinery, directly influencing system reliability and performance. Bearing failures, driven by stochastic degradation processes, necessitate accurate reliability assessment and maintenance optimization to minimize costs and downtime. This study proposes an adaptive inspection and maintenance model for rolling bearings based on the Delay Time Model (DTM), which captures the two-stage failure process: a normal operating stage until a hidden defect emerges, followed by a delay time until failure. The DTM leverages the failure delay time to schedule preventive maintenance, preventing costly failures. By modeling the defect initiation and delay time distributions using Weibull distributions, a maintenance cost model is developed to determine optimal periodic inspection intervals that minimize the long-term expected cost per unit time. A parameter estimation framework is established for both continuous and discrete inspection data, ensuring robust model applicability. The proposed approach is validated using a real-world run-to-failure dataset, demonstrating its effectiveness in optimizing maintenance schedules. Key contributions include the application of DTM to rolling bearing lifetime modeling, the formulation of a cost-effective inspection and maintenance strategy, and empirical validation through a case study. This work provides a practical framework for enhancing rolling bearing reliability and reducing maintenance costs.