Incorporating repair information into maintenance optimization models for repairable systems

Any organization that owns any large capital assets will eventually face a crucial decision - whether to repair or replace those assets, and when. This decision can have far-reaching consequences - replacing too early can mean a waste of resources, and replacing too late can mean catastrophic failure. The first is becoming more unacceptable in today´s sustainability-oriented society, and the second is unacceptable in the competitive marketplace. If large capital assets are analyzed as repairable systems, additional significant information can be incorporated into maintenance optimization models. Examples of such systems are power transformers in the electricity industry and haul trucks in the mining industry, among many others. When these assets break down, but have not yet reached their end-of-life, they can be repaired and returned to operating condition. However, these repairs often reduce the remaining useful life (RUL) of the system. The RUL of a system is an important factor in decision making for capital assets. If a company can correctly predict the remaining useful life of a repairable system, they can estimate the cost of maintaining and repairing the system until that point, or they can evaluate the potential benefits of replacing the entire system at a prior point. Standard methods of predicting the RUL often use condition monitoring data that companies may obtain as part of their regular maintenance practices. However, they often ignore or minimize the importance of repair information. It is expected that including this type of information can greatly improve RUL predictions upon further analysis. A number of other factors that must be considered when making economic decisions based on RUL will also be discussed. In this paper, we consider a proportional hazards model that includes a covariate based on the repair and maintenance information available. A case study is based on data from a major Canadian utility for power transformers. The covariate is s hown to improve the fit of a hazard model developed using EXAKT software.