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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 propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM).

In this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework.

The optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time.

A numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost.

The results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper.

Unlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.

This paper seeks to study maintenance policies on a plant‐wide level. It focuses on the effectiveness of condition‐based maintenance (CBM). It highlights the role of the production context and the importance of using appropriate metrics to assess CBM.

A simulation model was developed to explore the effects of production context using traditional performance indicators (costs and availability of each piece of equipment) and a more comprehensive metric (line efficiency).

The results showed that CBM has the best performance among other PM policies in loosely coupled processes. By contrast, in tightly coupled processes, CBM has a negative effect on the production line efficiency because it increases equipments' blockage and starvation states.

The simulation model was developed to reflect the reality. Nevertheless, some assumptions have been used to develop the conceptual and computerized model, which can be explored further in future research.

The idea of this paper originates from empirical findings of fellow researchers. The findings in this paper provide a better understanding of how CBM affects key performance indicators in different production contexts and therefore help managers to appropriately execute CBM programmes.

This study focuses on CBM from a new angle. The majority of the literatures on condition‐based maintenance either discusses pure technical issues, or focusses on single equipment only. In this research, the effectiveness of CBM for two processes is studied and CBM is compared with block and age‐based replacement policies using a comprehensive performance indicator.

This paper aims to present a case study where proportional hazards modeling software is used to evaluate the potential benefits of a condition‐based maintenance policy for military vehicle diesel engines.

Maintenance records for diesel engines were supplied by the UK Ministry of Defence. A proportional hazards model based on these data was created using EXAKT software. Covariate parameters were estimated using the maximum likelihood method and transition probabilities were established using a Markov Chain model. Finally, decision parameters were entered to create an optimal decision model.

Two significant covariates were identified as influencing the hazard rate of the engines. In addition, the optimal decision model indicated a potential economic saving of up to 30 per cent.

A model of this nature is particularly useful to predict failures, improve maintenance policies, and possibly reduce maintenance costs. In addition, the cost of implementing maintenance policies based on this model should be balanced with the potential to reduce the risk of danger to personnel.

The model presented provides military personnel with a decision tool that optimizes the maintenance policy for diesel engines installed in military vehicles.

A new policy for joint optimization of age replacement and spare provisioning has been proposed by incorporating a continuous review (s, S) type inventory policy, where s is the reorder level and S is the maximum stock level. Gives cost formulations for a single operating unit situation and outlines simulation procedure to determine optimal values of the decision variables by minimizing total cost of replacement and inventory. Studies the behaviour of this policy for a large number of case problems and highlights the effects of different cost elements, item failure characteristics and lead time characteristics. Also determines, for all case problems, optimal (s, S) policies to support Barlow‐Proschan age policy. Simulation results clearly indicate that the jointly optimal policy is more cost‐effective than Barlow‐Proschan policy.

The purpose of this paper is to determine a nearly optimal inspection sequence for a series system consisting of two components subject to gradual deterioration and whose failures are not self-announcing and can be detected only through inspection.

The problem is tackled in the context of condition-based maintenance (CBM) with a maintenance model in the class of the control-limit policies for which the maintenance decision is made following inspection by comparison of the deterioration level to critical thresholds. A mathematical model is developed to express the total expected cost per time unit as a function of the inspection instants.

For any given series system composed of two components with known critical deterioration threshold levels, and for any given set of costs related to inspection, inactivity due to failure, and preventive and corrective replacements of each component, a nearly optimal inspection sequence of the system is derived such as the total expected cost is reduced.

Due to the complexity of the cost model with the inspection instants (×1, ×2, ×3, …) being the decision variables, it has not been possible to derive the optimal solution. A quasi-optimal sequence of inspection times is derived along with the corresponding total average cost per time unit.

In many practical situations in which CBM is implemented, a tradeoff between inspection costs and inactivity and replacement costs has to be balanced when determining the intervals between successive inspections at which the degradation level of the components should be assessed and compared to predetermined critical threshold levels. Inspecting too often would increase inspection costs but in the same time it would also increase the probability to avoid a failure and end up with a preventive replacement, whereas not inspecting often enough would increase the probability to end up with a failure increasing replacement and inactivity costs.

While the inspection problem has been largely treated for single component systems, inspection policies become much more complex when considering multi-component systems. A two-component series system is considered in this paper.

The purpose of this paper is to address the issues whether or not minimal repair is effective for a repairable item with a known life distribution and when the minimal repair process should be stopped.

The life restoration degree (LRD) following minimal repair is defined and related to the shape parameter of the distribution so that a choice between minimal and perfect repairs can be made based on the shape parameter. Three replacement policies with minimal repair are considered and the corresponding decision rules are derived to determine when the minimal repair process should be stopped.

Main findings are: first, the LRD of minimal repair is inversely or approximately inversely proportional to the shape parameter, second, the effectiveness of minimal repair increases as the cost ratio of perfect and minimal repairs increases and the shape parameter decreases, and third, the unconditional mean residual life equal the mean time between the first and second failures.

The results can be easily used for maintenance strategy selection and maintenance decision optimization of repairable items.

The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions.

The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach.

The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time.

The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement.

The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.

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 chemical plant (CP) maintenance industry has been under increasing pressure by process designers to demonstrate its evaluation and information management of model checking (MC) on the durability’s performance and design of plant control instrument. This main problem has been termed as imperfect maintenance actions (IMAs) level. Although IMAs have been explored in interdisciplinary maintenance environments, less is known about what imperfect maintenance problems currently exist and what their causes are, such as the recent explosion in the Beirut city (4 August 2020, about 181 fatalities). The aim of this paper is to identify how CP maintenance environments could integrate MC within their processes.

To achieve this aim, a comprehensive literature review of the existing conceptualisation of MC practices is reviewed and the main features of information and communication technology tools and techniques currently being employed on such IMA projects are carried out and synthesised into a conceptual framework for integrating MC in the automation system process.

The literature reveals that various CP designers conceptualise MC in different ways. MC is commonly shaped by long-term compliance to fulfil the requirement for maintaining a comfortable durability risk on imperfect maintenance schemes of CP projects. Also, there is a lack of common approaches for integrating the delivery process of MC. The conceptual framework demonstrates the importance of early integration of MC in the design phase to identify alternative methods to cogenerate, monitor and optimise MC.

Thus far, this study advances the knowledge about how CP maintenance environments can ensure MC delivery. This paper highlights the need for further research to integrate MC in CP maintenance environments. A future study could validate the framework across the design phase with different CP project designers.