A lifetime estimation method for multi-component degrading systems with deteriorating spare parts

As one of the most important aspects in prognostics and system health management (PHM), spare parts play significant roles in enhancing reliability, shortening downtime, and reducing operating cost. However, the performance degradation of spare parts, though frequently encountered in industrial practice, has been rarely concerned during lifetime estimation and the according inventory optimization. Based on an amenable Wiener-process-based degradation model, this paper integrates the performance degradation of spare parts into lifetime estimation for stochastic multi-component degrading systems with congeneric key parts. Under two prevalent replacement policies, i.e., unit-by-unit replacement policy and batch-by-batch replacement policy, the lifetime distribution of the system considering the performance degradation of spare parts in storage, has been derived and the corresponding cost function for inventory management optimization has been constructed. Simulations and case study of an inertial navigation platform with three gyros are conducted to illustrate and validate the proposed method. The results show that the performance degradation of spare parts should be considered during inventory management, and the presented method can achieve an accurate lifetime estimation of the concerned system.

Automated summary

Spare parts are crucial in enhancing reliability, reducing downtime, and minimizing operating costs in prognostics and system health management (PHM). However, the performance degradation of spare parts is often overlooked in lifetime estimation and inventory optimization. This paper integrates the performance degradation of spare parts into lifetime estimation for stochastic multi-component degrading systems. The lifetime distribution considering the performance degradation of spare parts in storage is derived, and a cost function for inventory management optimization is constructed. Simulations and a case study validate the proposed method, highlighting the importance of considering performance degradation in inventory management.