JOINT OPTIMIZATION OF REPLACEMENT AND SPARE ORDERING FOR CRITICAL ROTARY COMPONENT BASED ON CONDITION SIGNAL TO DATE

It is widely accepted that condition-based replacement can not only make full use of components, but also decline inventory cost if the procurement of spare parts can be triggered upon accurate failure prediction. Most of the existing degradation or failure prediction models and approaches are population-based failures or suspensions, namely, to predict the failure time of a component, there are some failure or suspension histories of same type or similar components which can be used as reference. However, in practice, there exists the phenomenon in which no failure or suspension histories for some components can be used, what can be utilized is just the collected condition monitoring signals to date. In that case, failure time and probability are dcult to be estimated accurately. In this paper; a novel degradation prediction approach is introduced. Meantime, a new failure probability estimation function is developed based on component "service time" and "degradation extent" simultaneously. Then replacement and spare part ordering are jointly optimized according to the estimated failure probability. The optimization objective is to minimize long-run cost rate. Two bearing datasets are used to validate the proposed approach.