Analytical method for optimization of maintenance policy based on available system failure data

• An analytical optimization method for preventive maintenance (PM) policy is proposed.
• A new imperfect PM model is developed.
• The Weibull parameters are analytically estimated using maximum likelihood.
• The optimal maintenance interval and number of PM are also analytically determined.
• The model is validated by several numerical examples.

An analytical optimization method for preventive maintenance (PM) policy with minimal repair at failure, periodic maintenance, and replacement is proposed for systems with historical failure time data influenced by a current PM policy. The method includes a new imperfect PM model based on Weibull distribution and incorporates the current maintenance interval T0 and the optimal maintenance interval T to be found. The Weibull parameters are analytically estimated using maximum likelihood estimation. Based on this model, the optimal number of PM and the optimal maintenance interval for minimizing the expected cost over an infinite time horizon are also analytically determined. A number of examples are presented involving different failure time data and current maintenance intervals to analyze how the proposed analytical optimization method for periodic PM policy performances in response to changes in the distribution of the failure data and the current maintenance interval.