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The purpose of this paper is to explore whether or not a preventive replacement model always gives a solution with a specified preventive effect and looks at the relationship between the preventive effect and cost saving.

The age and block replacement policies with the Weibull failure distribution are considered. Measures of the preventive effect associated with specific maintenance policies are derived.

The sufficient condition to achieve a given preventive effect is represented graphically as a function of the system's aging intensity and the cost ratio of failure and preventive replacements.

The models developed in the paper will help maintenance engineers to know whether a system is aged or not and then they can make a decision on replacement.

The optimal replacement models considered in this paper give a solution with a good preventive effect only if the maintained system is sufficiently aged. The criterion whether or not the system is sufficiently aged is set by the decision‐maker based on specific maintenance situations or maintenance objectives.

The subject of investigation reported in this paper is the determination of an optimal replacement time for equipment that deteriorates with time. The following hypothesis is proposed and investigated. While a piece of equipment is in the final stages of its life span, i.e. the wear‐out phase, the application of preventive replacement strategy at constant time intervals reduces total down‐time. The novelty of the approach used in this research lies in the conversion of the more complicated classical constant‐interval replacement model to a simplified but nonetheless effective model. Results are shown for a case where the equipment time‐to‐failure has a normal distribution. These results also hold for a Weibull distribution with known shape and scale parameters. The simplified methods proposed in this paper can assist maintenance managers to better make economic decisions about equipment maintenance.

This paper seeks to develop and present a new mathematical formulation to determine the optimal preventive maintenance and replacement schedule of a system.

The paper divides the maintenance‐planning horizon into discrete and equally‐sized intervals and in each period decide on one of three possible actions: maintain the system, replace the system, or do nothing. Each decision carries a specific cost and affects the failure pattern of the system. The paper models the cases of minimizing total cost subject to a constraint on system reliability, and maximizing the system reliability subject to a budgetary constraint on total cost. The paper presents a new mathematical function to model an improvement factor based on the ratio of maintenance and repair costs, and show how it outperforms fixed improvement factor models by analyzing the effectiveness in terms of cost and reliability of the system.

Optimal decisions in each period over a planning horizon are sought such that the objectives and the requirements of the system can be achieved.

The developed mathematical models for this improvement factor can be used in theoretical and practical situations.

The presented models are effective decision tools that find the optimal solution of the preventive maintenance and replacement scheduling problem.

A common model in the age‐based replacement policy is based on the cost attribute and assumes that the model parameters are known. In practice, the model parameters are estimated from limited historical data, which brings uncertainty into the model. Moreover, minimizing the cost is not the only goal of the maintenance activity. From the decision maker's point of view, the multi‐attributes and the uncertainty of the age‐based replacement policy are two important aspects to take into consideration in the decision‐making process. The purpose of this paper is to propose an approach for a robust‐optimum multi‐attribute age‐based replacement policy.

The proposed approach is based on a combination of the multi‐attribute age‐based replacement policy and robust design problem philosophy. A case study is provided for illustrating the application of the proposed method.

It is found that the proposed approach can determine the interval time for preventive replacement that provides a robust and optimum solution for a multi‐attribute age‐based replacement policy.

The proposed approach can be used by the decision maker in determining a robust‐optimum interval time for preventive replacement of multi‐attribute age‐based replacement, a time interval which is not only optimum, but also robust.

This paper presents an approach that simultaneously considers the multi‐attributes and the uncertainty in the age‐based replacement policy which is, to date, not available.

The purpose of this paper is to properly plan the preventive maintenance schedule for multiple elevators and optimize the number of maintenance workers.The total amount of the maintenance cost consisting of the labor cost, the part cost, and the quality cost (the loss evaluated in terms of cost, to be incurred when an elevator breaks down) is to be minimized.The method is presented of setting up the optimal preventive maintenance schedule on a long‐term basis by rescheduling the contents of schedule dynamically and flexibly in accordance with the ever‐changing maintenance conditions, taking the possibility of the future occurrence of failure into consideration. From numerical experiments, the validity of the proposal procedure for planning the preventive maintenance schedule and the effectiveness of considering the possibility of the future occurrence of failure in planning the schedule are shown, and the optimal number of maintenance workers can be decided.

We formulate a multi‐attribute decision model for preventive replacement of a “magnetic sealing head” in a soft‐drink producing factory in the Kingdom of Saudi Arabia. In case of failure of this part, the opportunity cost (for production losses) is very important, as the entire production line will be idle. We determine in a first case the replacement policy that minimizes the total expected unit cost of replacement (preventive and corrective). Next, we determine the optimal policy that maximizes the expected multi‐attribute utility of the decision‐maker in the factory. Four attributes are considered in the replacement problem, namely cost, quality, labor productivity, and cash flow availability. The optimal policy in each case outperforms by far the one applied in the plant, which turns out to be costly and inefficient with respect to the utility of the decision‐maker.

The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.

A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.

The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.

In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.

In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

This paper aims to address the effect of inspection intervals on cost function in condition‐based maintenance (CBM) and show how selecting an appropriate inspection scheme may reduce the cost associated to a CBM program.

In CBM, replacement policy is often defined as a threshold for replacement or leaving an item in operation until next inspection, depending on monitoring information. The control limit replacement policy framework, already reported by some research referred to in this paper, is utilized to determine the optimal replacement threshold. Having released the assumption that the inspections are performed at fixed and constant intervals, an iterative procedure is proposed to evaluate alternative inspection schemes and their associated total average cost of replacements and inspections.

The paper proposes an approach in which preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal replacement policy and an age‐based inspection scheme such that the total average cost of replacements and inspections is minimized.

In many practical situations where CBM is implemented, e.g. manufacturing processes, inspections require labor, specific test devices, and sometimes suspension of the operations. Thus, when inspection cost is considerable, by applying the proposed approach, one can obtain an inspection scheme that reduces the cost.

Using the approach proposed in the paper, a cost‐effective age‐based inspection scheme for a system under CBM is determined.

The purpose of this paper is to investigate age replacement policies for two-component parallel system with stochastic dependence. The stochastic dependence considered, is modeled by a one-sided domino effect. The failure of component 1 at instant t may induce the failure of component 2 at instant t+τ with probability p _1→2. The time delay τ is a random variable with known probability density function h _{p 1→2} (.). The system is considered in a failed state when both components are failed. The proposed replacement policies suggest to replace the system upon failure or at age T whichever occurs first.

In the first policy, costs and durations associated with maintenance activities are supposed to be constant. In the second replacement policy, the preventive replacement cost depends on the system’s state and age. The expected cost per unit of time over an infinite span is derived and numerical examples are presented.

In this paper and especially in the second policy, the authors find that the authors can get a more economical policy if the authors consider that the preventive replacement cost is not constant but depends on T.

In this paper, the authors take into account of the stochastic dependence between system components. This dependence affects the global reliability of the system and replacement’s periodicity. It can be used to measure the performance of the system et introduced into design phase of the system.

The purpose of this paper is twofold: an approach is proposed to determine the optimum replacement time for shovel teeth; and a risk-quantification approached is developed to derive a confidence interval for replacement time.

The risk-quantification approach is based on a combination of Monte Carlo simulation and Markov chain. Monte Carlo simulation whereby the wear of shovel teeth is probabilistically monitored over time is used.

Results show that a proper replacement strategy has potential to increase operation efficiency and the uncertainties associated with this strategy can be managed.

The failure time distribution of a tooth is assumed to remain “identically distributed and independent.” Planned tooth replacements are always done when the shovel is not in operation (e.g. between a shift change).

The proposed approach can be effectively used to determine a replacement strategy, along with the level of confidence level, for preventive maintenance planning.

The originality of the paper rests on developing a novel approach to monitor wear on mining shovels probabilistically. Uncertainty associated with production targets is quantified.