OPTIMAL PERIODIC REPLACEMENT USING ACCELERATED LIFE TESTING FOR INVERTED WEIBULL DISTRIBUTION

Planned maintenance of facilities, equipment, and systems with periodic replacements is designed to improve equipment life and avoid any unplanned maintenance activities, helping to reduce equipment downtime and reduce the number of major repairs, reduce overtime costs and safety and quality conditions are improved for all. In this paper, we estimate the optimal periodic replacement for repairable and non-repairable products that minimize the total replacement cost and replacement cost rate. The investigations concern the estimation of parameters. Inverted Weibull distribution using the power rule model for constant stress in accelerated life tests. The maximum likelihood method is used to estimate the model and life distribution parameters from the test data when Type II censoring is applied. The confidence interval for the model parameters is constructed using the normal approximation and the bootstrap method. We use the estimated parameters to achieve optimal periodic replacement of repairable and non-repairable products, which minimizes the total cost of replacement. Finally, some numerical illustrations are provided. We find the confidence interval length for the estimated parameters in bootstrap method for the shape parameter of IW distribution greater than it in normal approximation while for scale parameter is vice verse. Furthermore, the optimal periodical replacement for nonrepairable or repairable products is increasing as increasing the values of the estimated parameters