Abstract
A repairable system operates under a maintenance strategy that calls for complete preventive repair actions at prescheduled times and minimal repair actions whenever a failure occurs. Under minimal repair, failures are modeled according to a nonhomogeneous Poisson process. When the intensity function is assumed to grow proportional to a power of time, this process is called the power law process. Assuming that the system will be in operation for an infinite time, we find the expected cost per unit of time for each preventive maintenance policy and hence obtain the optimal strategy as a function of the intensity function of the process. Large-sample procedures to estimate the optimal maintenance check points for the power law process are also discussed. The results are applied in a real data set concerning failures histories of power transformers.
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