Sequential Preventive Maintenance Model Research Articles

Preventive Maintenance Models Based on the Generalized Geometric Process

For a generalized geometric process (GGP), the geometric ratio changes with the number of repairs rather than a constant. The GGP is a more flexible maintenance model that can reflect the varying of the repair effect to the system. On the basis of the GGP, two imperfect preventive maintenance (PM) models are studied. First, we assume that the system is preventively maintained periodically after every $T$ time unit, and is correctively repaired at failure. The corrective repair is a minimal repair that just restores the system to work, while the PM results in a GGP, i.e., the lifetime sequence of a system after PM constitute a decreasing GGP. The long-run average cost rate $C(N,T)$ of the system is derived, and the optimal bivariate policy $(N^*,T^*)$ is determined by minimizing $C(N,T)$ , where $N$ is the number of PMs before replacement. Next, a sequential PM model is investigated for a system in which a sequential time interval to be determined for PM. An algorithm is given to seek the optimal replacement policy $N^*$ and the optimal time intervals $T_1^*,T_2^*,\ldots, T_N^*$ between PMs. By assuming that the lifetime of the system is Weibull distributed, the optimal policy is obtained explicitly. In both models, numerical examples are provided to verify the effectiveness of the approaches developed.

Modeling and optimizing maintenance schedule for energy systems subject to degradation

In recent years, with the increasing investment on distributed energy system (DES), maintenance management has played an important role in improving the system performance. This paper aims to integrate the conception of the multiple attribute value theory and imperfect maintenance into an improved sequential preventive maintenance (PM) model for the DES subject to degradation. A model-iteration algorithm using a multi-attribute model (MAM) is developed to optimize the maintenance schedule cycle by cycle. The optimal PM interval for each cycle is obtained through MAM, by maximizing the equipment availability and minimizing the maintenance cost simultaneously. A case study is investigated to show that this sequential PM model is much more significant than conventional PM models. Finally, the sensitivity of the proposed method is investigated.

Model for imperfect age-based preventive maintenance with age reduction

The effect of ageing on the deterioration rate of most repairable systems cannot be ignored. Preventive maintenance (PM) is performed in the hope of restoring fully the performance of these systems. However, in most practical cases, PM activities will be only able to restore part of the performance. Bridging the gap between theory and practice in this area requires realistic modelling of the effect of PM activities on the failure characteristics of maintainable systems. Several sequential PM models have been developed for predetermined PM interval policies but much less effort has been devoted to age-based ones. The purpose of this paper is to develop an age-based model for imperfect PM. The proposed model incorporates adjustment factor in the effective age of the system. The system undergoes PM either at failure or after a predetermined time interval whichever of them occurs first. After a certain number of such PMs, the system is replaced. The problem is to determine both the optimal number of PMs and the optimal PM's schedule that minimize the total long-term expected cost rate. Model analysis relating to the existence and uniqueness of the optimal solutions is provided. Numerical examples are presented to study the sensitivity of the model to different cost function's factors and to illustrate the use of the algorithm.

On an imperfect preventive maintenance model

Presents two sequential preventive maintenance (PM) models. The first model can be viewed as an extension of Nakagawa’s model where the age reduction of the system is assumed to depend on the level of PM activities. Linear and nonlinear relationships between age reduction and PM level have been considered. In the second model, a sequential PM model is developed in which PM intervals are defined such that the integrated hazard rate over each interval is the same for all intervals. This restriction reduces the number of decision variables from N + 2 to only three decision variables, where N is the number of intervals between PMs. The cost difference between these two models was very small for the examples solved. This provides evidence that the integrated hazard rate restriction provides a very good approximation of this sequential PM model. This suggests that the integrated hazard rate condition may be used to reduce greatly the number of decision variables at the expense of a reasonable increase in the expected total cost.