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The purpose of this paper is to focus on an optimizing maintenance policy with repair limit time for a new type of aircraft component, in which the lifetime is assumed to be an uncertain variable due to no historical operation data, and the repair time is a random variable that can be described by the experimental data.

To describe this repair limit time policy over an infinite time horizon, an extended uncertain random renewal reward theorem is firstly proposed based on chance theory, involves uncertain random interarrival times and stochastic rewards. Accordingly, the uncertain random programming model, which minimized the expected maintenance cost rate, is formulated to find the optimal repair limit time.

A numerical example with sensitivity analysis is provided to illustrate the utility of the proposed policy. It provides a useful reference and guidance for aircraft optimization. For maintainers, it plays an important guiding role in engineering practice.

The proposed uncertain random renewal reward process proved useful for the optimization of maintenance strategy with maintenance limited time for a new type of aircraft components, which provides scientific support for aircraft maintenance decision-making for civil aviation enterprises.

When the system fails, the decision to repair or replace a failed unit may depend on the estimated repair cost. Such an idea is called repair limit replacement policy. The repair limit is a limit on the amount of money which can be spent on the repair of a system. The repair limits thus provide an economic replacement policy. Examines optimal repair‐cost limits for a Weibull‐distributed time to failure and an exponentially distributed repair cost. Derives bounds for the optimal repair cost limit that minimizes the average cost per unit time for repairs and replacement. With those bounds, develops a simple algorithm to obtain the optimal repair‐cost limit. Gives numerical examples to illustrate the algorithm.

In this paper, we consider a repair‐time limit replacement problem with imperfect repair and develop a graphical method to determine the optimal repair‐time limit which minimizes the expected total discounted cost over an infinite time horizon. The method proposed can be applied to an estimation problem of the optimal repair‐time limit from the empirical repair‐time data. Then, the modified scaled total time on test transform of the underlying repair‐time distribution function is used. Numerical examples are devoted to examine asymptotic properties of the nonparametric estimator for the optimal repair‐time limit.

Examines optimal cost limits for a Weibull‐distributed time to failure and an exponentially distributed repair‐cost limit policy which minimizes the average cost per unit time for repairs and replacement. Presents numerical examples to illustrate the algorithm.

The objective of the paper is to consider the problem of the strength of a manufactured item against stress, when the component follows Weibull failure law. Different cases of stress and strength with varying parameters are discussed for the Weibull‐Weibull stress‐strength model considered in this paper. The application of the proposed technique will help in understanding the design methodology of the system and addressing the risks involved in perceived quality and reliability levels by eliminating or at least reducing the risk impact at the design phase.

Generalised Weibull‐Weibull stress‐strength models have been analysed for different cases of shape parameters for stress and strength to estimate the reliability of the system. The model is generalized using semi‐regenerative stochastic processes with the help of a state space approach to include a repair facility.

Different cases of stress and strength with varying parameters have been discussed for the Weibull‐Weibull stress‐strength models considered in this paper. The results show how the stress‐strength reliability model is affected by changes in the parameters of stress and strength. The application of the proposed technique will help in understanding the design methodology of the system, and also lead to the problem of addressing the risks involved in perceived quality and reliability levels by eliminating or at least reducing the risk impact in the design phase.

The present study is limited to a few special cases of Weibull‐Weibull stress‐strength models. The authors propose to continue to study the behaviour of general Weibull strength against exponential stress in particular and to identify the shape parameter that maximises the strength reliability.

The application of the proposed technique will help in understanding the design methodology of the system, and also lead to the problem of addressing the risks involved in perceived quality and reliability levels by eliminating or at least reducing the risk impact at the design phase. The model has been extended and generalized to include a repair facility under the assumption that all the random variables involved in the analysis are arbitrarily distributed (i.e. general).

In the Weibull‐Weibull stress‐strength model of reliability, different cases have been considered. In the first case, both parameters of stress‐strength have the same values and are independent of the distribution. In the second case, if the shape parameter of the strength is twice that of the stress, the probability will have a normal distribution with different parameter values. In the third case, if the shape parameter of the stress is twice that of the strength, then probability distribution is a parabolic cylindrical function. The study shows how to proceed in all cases. The model is generalized to include a repair facility, with all the random variables involved in the analysis being arbitrarily distributed using semi‐regenerative stochastic processes.

The purpose of this paper is to present the development of a maintenance engineering policy in the context of a decision support model based on a production machine process perspective.

The structure of the policy is called the maintenance decision support (MDS) model, which consists of three steps: initial setup, deterioration monitoring, and decision making. A detailed presentation of each step of the proposed model together with a real case example from the pulp manufacturing industry proves the applicability of the model.

Validation of the proposed MDS model is as follows. In Task 1 of Step 1, the cutting, sealing, and perforating line processes are classified as critical machining processes. The analysis of Task 2 of Step 1 found that cutting knife, bearing, and motor are classified as the components that most possibly contribute to the cutting appearance quality. In Task 3 of Step 1, it was found that the cutting knife is classified as a maintenance-significant component with non-repairable and single-component type characteristics. The result of Step 2 suggested that at the 29th hour of operating time, the decision of do-something was suggested. In the following step (Step 3), for the case of the cutting knife, which has been classified as a non-repairable type component, the decision to perform preventive replacement of cutting knife is recommended to be carried out at the 29th hour of operating time.

The uniqueness of this model is that it systematically considers different machinery component(s) characteristics, including single- and multiple-component cases, repairable and non-repairable types, and functional or/and physical failure types, to make maintenance decisions.

The proposed MDS model provides a systematic guideline for identifying, evaluating, and monitoring, which makes maintenance-related decisions. Three significant maintenance decisions can be determined based on the proposed MDS model, which includes an appropriate time-to-perform maintenance, correct maintenance actions to be performed, and the right component required for maintenance (for multi-component cases).

One of the vital elements in considering the production machine process perspective toward the development of the MDS model is the need to use product output/quality characteristics for machine deterioration-monitoring and decision-making processes.

The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc. Within each category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.

Literature on classification of maintenance management has so far been very limited. This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitably.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of maintenance management

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.

The expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.

The authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.

The consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.

This study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.

The paper aims to explore unavailability of dormant systems that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure based maintenance model, where full renew is realized at the occurrence of every nth failure. It proposes the imperfect corrective maintenance model, where each restoration process deteriorates the system lifetime, probability distribution of which is gradually changed via increasing failure rate.

Basic reliability mathematics necessary for unavailability quantification of a system which undergoes a real aging process with maintenance has been derived proceeding from renewal theory. New renewal cycle was defined to cover the real aging process and the expectation of its length was determined. All events resulting in the failure of studied system were explored to determine their probabilities. An integral equation where the unavailability function characterizing studied system is its solution was derived.

Preventive maintenance is closely connected with the occurrence of the nth failure, which starts its renew. The number n can be considered as a parameter which significantly influences the unavailability course. The paper shows that the real aging process characterized by imperfect repairs can significantly increase the unavailability courses in contrast with theoretical aging. This is true for both monitored and dormant systems.

Although mathematical methods used in this article were inspired and influenced by the work of reference (van der Weide and Pandey, 2015), derivation of final formulas for unavailability quantification considering the new renewal cycle is original. Idea of the real aging process is new as well. This paper fulfils an identified need to manage the maintenance of realistically aging systems.

Considers a system with two states ‐ operating and failed. The system undergoes overhauls preventively at scheduled times and the successive overhaul times constitute non‐decreasing geometric process. Assumes that the failure rate increases with the number of overhauls. Also assumes that the system undergoes only minimal repairs if it fails between optimal number of overhauls N*, the optimal time T* and the optimal pair (N*, T*) based on the cost considerations. Gives numerical examples to illustrate the procedure.