This paper deals with two optimal preventive maintenance policies, i.e., Policy I and Policy II proposed by R. Barlow and L. Hunter, and in particular emphasizes on the characteristics of the optimal maintenance theories due to numerical analysis. Through this paper, the Weibull model is adopted as a failure modes and its shape parameter β plays an important role. According to the results newly obtained, we discuss the characteristics of the two types of policies and their advantages and disadvantages. Next, the new results are applied to the field data of failure and maintenance in order to investigate the actural maintenance techniques.

There are two distinct types of maintenance action, namely preventive one and corrective one. Preventive maintenance is performed at regular intervals and can contribute significantly towards the increase of reliability and availability. It must be scheduled carefully in order that the availability is maximized through optimizing regular interval. On the other hand, corrective maintenance is performed when the system fails, and so the occurrence of corrective maintenance action is a random variable that cannot be predicted beforehand. From these considerations, it is clear that time is the most important factor in maintainability, and therefore, we classify maintenance data into two groups, that is, scheduled maintenance data and unscheduled maintenance one. Next, based on these classified data, we propose the new availability model which modifies Policy II proposed by R. Barlow and L. Hunter. Finally, we show the usefulness of the new model proposed here by applying these theoretical results to real data of some power plant.

The purpose of this note is to carry out study on a 3-out-of-4:G warm standby system with nonidentical components. By using Markov model, the general form solution of stationary availability of the system is obtained. Examples are given to illustrate the solutions of transient and stationary availability of such system.

This letter deals with the reliability function in the case of periodic preventive replacement of items in order to increase MTBF, that is, two replacement policies; strictly periodic replacement (SPR) and randomly periodic replacement (RPR). We stress on simple introduction of the reliability theory under preventive replacement policies using the Laplace transform and obtain the theoretical results of SPR and RPR. Then these results are applied to the Weibull distribution and finally in order to show useful information of preventive replacement, the numerical results of SPR are provided.