Condition-based maintenance is growing in popularity as a means of improving equipment maintenance efficiency. Whether it be the maintenance of an airplane, a computer system, or any type of physical system, the prognostic tools associated with condition-based maintenance are subject to statistical error. These errors can lead to unnecessary preventive maintenance due to underestimation of system remaining life and unnecessary system failures due to overestimation of system remaining life. What is not clear is if these statistical errors outweigh the benefits of a condition-based maintenance policy. This study attempts to address this concern through the evaluation and comparison of three maintenance policies for a simple system. The maintenance policies are run-to-failure, scheduled preventive maintenance and condition-based maintenance. A discrete event simulation model is used to estimate the average time between successful missions for the system under each of these policies. An extensive set of numerical experiments is used to analyze system performance under a wide variety of operating conditions. The results suggest that condition-based maintenance can improve system performance as much as 10% to 15% beyond that achieved using scheduled preventive maintenance. However, the results also suggest that moderate statistical error can render condition-based maintenance inferior to scheduled maintenance and severe statistical error can render condition based maintenance inferior to run-to-failure. In addition to the results obtained by this study, the methodology used herein can aid maintenance managers in moving from a scheduled maintenance philosophy to a just-in-time maintenance philosophy; thereby increasing the availability of affected systems. Increasing the availability of any system is given considerable importance especially by industries that serve people. For example, in the airline and health industries the availability of a system is vital since any associated down time results in large profit losses and customer dissatisfaction. Overall, the method presented herein can help any kind of industry in developing a way for assessing their maintenance policies which could help them improve the availability of their systems in the future.
Carrasco, M. (2006). A Study of the Impact of Prognostic Errors on System Performance. Inquiry: The University of Arkansas Undergraduate Research Journal, 7(1). Retrieved from https://scholarworks.uark.edu/inquiry/vol7/iss1/15