Intelligent sensor impact on predictive maintenance program costs (Q2247419)

Summary: In this work, we develop a simulation study based on economic optimisation to compare the economical impact of two maintenance policies, traditional failure maintenance policy with predictive maintenance policy that utilises intelligent network sensor information. The simulation study established in this work compare tow maintenance strategies: predictive maintenance and failure-based maintenance, in order to compare when it is less expensive to maintain the equipment before it breaks down using intelligent network sensors than to replace it after its breakdown, to sum up, if it is profitable to implement this new technology. Also with this proposed approach, the decision maker could be in the position to decide on a most appropriate economical framework for the optimum cost, based on the comparison between breakdown cost and the cost of sensors. The method can be used by companies to make a decision when considering implementing remote monitoring. To illustrate the use and the advantages of the proposed maintenance policy, a numerical example is investigated.