Critical component life prediction and cost estimation for decision support in remanufacturing

In remanufacturing industry, an efficient and effective decision making system is always needed to decide whether a used component should be repaired or replaced. The principle issue is to design a decision making strategy that can accommodate various application scenarios. In this paper, a generic framework is proposed for decision making in remanufacturing. The proposed approach is characterized by its ability to assist decision making when component condition is in the gray area, i.e., the condition is close to specification yet not exceeded. A case study based on cylinder bore in diesel engines shows that this framework is able to predict the remaining life of used components and give decision advice based on life-cycle cost estimation. This decision making system is also featured by its ability to improving the physical model based on the knowledge learned from real data. For example, in the case of cylinder bores, the wear rate is often associated with the specific operating conditions of the engines. In this case study, it is shown that this framework could regulate the parameters of the physical model according to data from different application scenarios and therefore offers better performances in decision making.