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The selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP. [ABSTRACT FROM AUTHOR]

Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework. [ABSTRACT FROM AUTHOR]

• A new SM model of the CDS with multi-different and S-dependent components is studied. • The diverse and dependent component degradation is depicted by an extended DRI model. • Explicit reliability functions of systems with arbitrary configurations are provided. • Examples verified the great effects of multi-difference and S-dependency on strategy. Selective maintenance (SM) is often performed on systems having limited maintenance resources to enhance mission reliability. In practical engineering, components within a system are typically distinct and correlated, leading to differences in the degradation processes and sensitivity to working conditions, as well as stochastic dependencies (S-dependencies) among the components. However, in most studies on the SM of continuously degrading systems (CDSs), these features are not considered, thereby limiting the accuracy of reliability evaluations and the efficiency of maintenance strategies. Therefore, considering the differences and S-dependencies of components, we investigate SM optimization for CDSs. Focusing on the diverse and stochastically dependent degradation processes of non-identical components, we first propose an extended degradation rate interaction model integrated with a general stochastic process to describe the multiple degradation processes of components and manifest the sensitivity differences to working conditions in the degradation states and rates. Next, a derivation method is developed to obtain explicit mission reliability functions for systems with arbitrary configurations. Subsequently, an SM optimization model that incorporates the effects of multiple differences and S-dependencies of the components is formulated and used to obtain an optimal maintenance strategy that considers resource and mission reliability constraints. Finally, the effectiveness of the proposed method is demonstrated using two numerical examples. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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This paper introduces a solution method for the multimission selective maintenance problem (SMP) that combines column-generation (CG) and genetic algorithms (GAs). The multimission SMP is an optimisation problem that arises when a system performs a sequence of missions separated by breaks of finite duration. During these finite breaks, only a subset of possible maintenance actions can be performed due to resource limitations. The problem is in deciding what actions to perform during each break duration such that the system meets or exceeds a minimum target reliability for all missions. The resulting optimisation problems are usually modelled as mixed integer nonlinear mathematical programmes, which are hard to solve. They are usually solved using metaheuristics. We propose a solution method based on CG framework in which the subproblems are solved using a GA. By integrating the GA within the classical CG framework, high-quality solutions can be obtained very quickly. The proposed solution method is capable of solving systems composed of both parallel and k-out-of-n:G subsystems. This hybrid CG algorithm is shown to obtain near optimal solutions and outperform other metaheuristic solution methods; it is also shown to be capable of solving large-scale systems composed of many subsystems and hundreds of components in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

With the increasing energy loss and environmental pollution caused by engineering systems, energy conservation and environmental protection have received more attention in industry environments. Based on this consideration, this paper introduces energy loss and environmental effect into the selective maintenance for a multistate system. According to the relationship between energy loss and system degradation, as well as the relationship between environmental pollutant discharge and system degradation, the energy loss and environmental effect models are proposed, respectively. With these models used as a basis, the selective maintenance model for a multistate system that considers energy loss and environmental effect is constructed, and a differential evolution algorithm is employed to solve the resulting maintenance strategy optimization problem. Finally, with a coal transportation system taken as an example, the energy loss and environmental effect models proposed in this paper are verified by comparison with the Monte-Carlo simulation method. Based on this, analyses of the selective maintenance strategy optimization are carried out to demonstrate the effectiveness of the proposed selective maintenance model and optimization method. The analysis results have engineering significance for how to introduce energy loss and environmental effect into the selective maintenance strategy optimization for a multistate system.

The maintenance strategies commonly employed in industrial settings primarily rely on theoretical models that often overlook the actual operating conditions. To address this limitation, the present paper introduces a novel selective predictive maintenance approach based on a machine learning model for a multi-parallel series system, which involves executing multiple missions with breaks between them. For this purpose, the proposed selective maintenance approach consists of finding, at each breakdown, the optimal structure of maintenance activities that provide the desired reliability level of the system for each mission. This decision is based on a component's actual age, as determined by the prediction model. In addition, an optimization model with the Extended Great Deluge (EGD) algorithm uses these predictions as input data to identify the best maintenance level for each component considering the constrained maintenance resources. Finally, the numerical results of the proposed idea applied to the Flexible Manufacturing System (FMS) data are presented to show the robustness of the model. [ABSTRACT FROM AUTHOR]

Systems in the industry are often required to execute a sequence of missions, and the state of the multi‐state systems may deteriorate with the increasing of running time. To improve the system reliability of the next mission, selective maintenance is applied in the limited break. Due to the fatigue effect, the work rate of the repairperson decays with time. However, the existing selective maintenance studies do not consider the impact of fatigue effect on work rate, which leads to underestimating the duration of maintenance actions and overestimating the system reliability. To overcome the above problems, a new selective maintenance model for multi‐state systems is developed to maximize the system reliability of the next mission. First, the decay process of the work rate of a repairperson is described as a piecewise continuous decreasing exponential function of working time. Next, a homogenous discrete‐state continuous‐time Markov process is used to describe the performance capacity degradation of components. Considering the stochastic break duration, based on the state probability distribution of each component obtained by the Markov model, the universal generation function (UGF) method is applied to evaluate the multi‐state system reliability. Then, taking the selected maintenance actions and maintenance priority as decision variables, the resulting selective maintenance optimization problem is solved by a tailored genetic algorithm. Finally, two computational experiments are carried out to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A type of multistate system executes multiple consecutive missions over a period, and maintenance activities are also performed sequentially during this period, to improve system reliability during the consecutive missions. Sequential maintenance and dependence on the system state introduce new challenges for the calculation of system reliability during consecutive missions, which makes selective maintenance difficult to perform. In order to provide theoretical support for the selective maintenance of this type of multistate system and obtain a reasonable and effective selective maintenance strategy, a reliability calculation method is proposed for a multistate system that executes multiple consecutive missions under sequential maintenance. Based on this, a new selective maintenance model for a multistate system executing multiple consecutive missions is developed, and the ant colony optimization algorithm is customized to address the resulting maintenance strategy optimization problem. Finally, using an oil transportation system as an example, the accuracy and computational effectiveness of the proposed reliability calculation method are verified by comparison with the Monte–Carlo simulation method. Moreover, the selective maintenance strategy optimization is performed on the oil transportation system to illustrate the effectiveness of the proposed selective maintenance model and the customized optimization method, and the results can provide practical guidance for the reasonable adjustment of mission durations.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Selective maintenance is an important condition-based maintenance strategy for multi-component systems, where optimal maintenance actions are identified to maximize the success likelihood of subsequent missions. Most of the existing works on selective maintenance assumed that after each mission, the components' states can be precisely known without additional efforts. In engineering scenarios, the states of the components in a system need to be revealed by inspections that are usually inaccurate. Inspection activities also consume the limited resources shared with maintenance activities. We, thus, put forth a novel decision framework for selective maintenance of partially observable systems with which maintenance and inspection activities will be scheduled in a holistic and interactively sequential manner. As the components' states are partially observable and the remaining resources are fully observable, we formulate a finite-horizon Mixed Observability Markov Decision Process (MOMDP) model to support the optimization. In the MOMDP model, both maintenance and inspection actions can be interactively and sequentially planned based on the distributions of components' states and the remaining resources. To improve the solution efficiency of the MOMDP model, we customize a Deep Value Network (DVN) algorithm in which the maximum mission success probability is approximated. A five-component system and a real-world multi-state coal transportation system are used to demonstrate the effectiveness of the proposed method. It is shown that the probability of the system successfully completing the next mission can be significantly increased by taking inspections into account. The results also demonstrate the computational efficiency of the customized DVN algorithm. [ABSTRACT FROM AUTHOR]

A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particle swarm optimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

The maintenance strategies commonly employed in industrial settings primarily rely on theoretical models that often overlook the actual operating conditions. To address this limitation, the present paper introduces a novel selective predictive maintenance approach based on a machine learning model for a multi-parallel series system, which involves executing multiple missions with breaks between them. For this purpose, the proposed selective maintenance approach consists of finding, at each breakdown, the optimal structure of maintenance activities that provide the desired reliability level of the system for each mission. This decision is based on a component’s actual age, as determined by the prediction model. In addition, an optimization model with the Extended Great Deluge (EGD) algorithm uses these predictions as input data to identify the best maintenance level for each component considering the constrained maintenance resources. Finally, the numerical results of the proposed idea applied to the Flexible Manufacturing System (FMS) data are presented to show the robustness of the model.

This paper addresses the joint selective maintenance and repairperson assignment problem (JSM–RAP) for complex multicomponent systems. The systems perform consecutive missions separated by scheduled finite duration breaks and are imperfectly maintained during the breaks. Current selective maintenance (SM) models usually assume that only one repair channel is available or that the repairperson assignment optimisation can be done at a subsequent stage. Using a generalised reliability function for k-out-of-n systems, we formulate the JSM–RAP for multicomponent systems more complex than the series-parallel systems commonly used in previous SM models. Two nonlinear formulations and their corresponding binary integer programming models are then proposed and optimally solved. Numerical experiments show the added value of the proposed approach and highlight the benefit of jointly carrying out the selection of the components to be maintained, the maintenance level to be performed and the assignment of the maintenance tasks to repairpersons. It is also shown that the flexibility provided by mixed skill cohorts of repairpersons over uniform cohorts can yield higher performance levels when the skillsets are significantly different. [ABSTRACT FROM AUTHOR]

Many industrial systems require a chain of identical missions to be performed with finite breaks between every two missions. During these breaks, the system maintenance actions (repair, replacement and preventive maintenance) are performed to enhance the reliability of the system for the next mission. This paper discusses a series-parallel system in which each subsystem consists of two types (i.e., repairable or replaceable) of functionally identical mission components arranged in parallel combination. We propose a selective maintenance model to suggest the optimal policy of minimal repair of failed repairable components, replacement of failed replaceable components, and replacement of functioning components (preventive maintenance) to optimize the system reliability. A simulated numerical data is taken and solved using CPLEX solver to demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

For industrial and military applications, a sequence of missions would be performed with a limited break between two adjacent missions. To improve the system reliability, selective maintenance may be performed on components during the break. Most studies on selective maintenance generally use minimal repair and replacement as maintenance actions while break duration is assumed to be deterministic. However, in practical engineering, many maintenance actions are imperfect maintenance, and the break duration is stochastic due to environmental and other factors. Therefore, a selective maintenance optimization model is proposed with imperfect maintenance for stochastic break duration. The model is aimed to maximize the reliability of system successfully completing the next mission. The reinforcement learning(RL) method is applied to optimally select maintenance actions for selected components. The proposed model and the advantages of the RL are verified by three case studies verify. [ABSTRACT FROM AUTHOR]

The maintenance priority indexes-based (MPIs-based) selective maintenance policy (SMP) for a complex system with degradation components is studied in this work. The SMP is performed in a scheduled break when the system just completed the current mission. During the break, the MPIs, including maintenance importance, structure importance, and reliability importance, are comprehensively considered to support the system for executing the next mission. The objective of the MPIs-based SMP is to find the optimal maintenance decision for the system subject to maintenance cost and time constraints. Besides, the maintenance quality related to component degradation, as well as the economic dependence, are also considered as the maintenance information to be integrated when performing a maintenance decision. The complicated optimization problem is solved by using a simulated annealing algorithm (SAA). The simulation example of an aero-engine control system is presented to depict the maintenance decision process and illustrate the advantage of MPIs-based SMP.

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In several industrial fields like air transport, energy industry and military domain, maintenance actions are carried out during downtimes in order to maintain the reliability and availability of production system. In such a circumstance, selective maintenance strategy is considered the reliable solution for selecting the faulty components to achieve the next mission without stopping. In this paper, a novel multi-level decision making approach based on data mining techniques is investigated to determine an optimal selective maintenance scheduling. At the first-level, the age acceleration factor and its impact on the component nominal age are used to establish the local failures. This first decision making employed K-means clustering algorithm that exploited the historical maintenance actions. Based on the first-level intervention plan, the remaining-levels identify the stochastic dependence among components by relying upon Apriori association rules algorithm, which allows to discover of the failure occurrence order. In addition, at each decision making level, an optimization model combined to a set of exclusion rules are called to supply the optimal selective maintenance plan within a reasonable time, minimizing the total maintenance cost under a required reliability threshold. To illustrate the robustness of the proposed strategy, numerical examples and a FMS real study case have been solved. [ABSTRACT FROM AUTHOR]

With the development of complex equipment which integrates mechanical, electrical, hydraulic and other technologies towards the direction of high reliability, long life and high cost, small sample failure data, even no failure data, appears in the reliability timing censoring test frequently. On the basis of explaining the characteristics of machine-level and system-level complex equipment with multi-source reliability data, uncertain data information, and continuous execution of tasks, this paper summarizes the research status of reliability evaluation of small sample failure data from three aspects of prior information, data expansion and multi-source information fusion. The shortcomings of the existing research are summarized, and the development trend and research focus of reliability assessment of complex equipment in the future are put forward. A new idea of applying Bayesian network to quantify the uncertainty in complex equipment is proposed, and the research of selective maintenance optimization on group equipment based on reliability assessment is carried out.

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break.Alist of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme. [ABSTRACT FROM AUTHOR]

The mission for a type of transportation system is to complete a certain transportation volume within a predetermined period, during which maintenance is also performed. In such a system, any unit selected to be maintained is often connected to the system to participate in the system operation immediately after its maintenance, and its participation in the system operation at different moments has different effects. Based on this consideration, this paper proposes a new selective maintenance model for a multi-state system wherein the maintenance sequence arrangement is considered. In this selective maintenance problem, to maximise the system reliability, a sub-set of all desired maintenance activities is selected, and the sequence of the selected maintenance activities to be performed is arranged, with the transportation volume requirement and the limited budget considered. An ant colony optimisation algorithm is tailored for the resulting optimisation problem. Two example analyses are presented to illustrate the effectiveness of the tailored ant colony optimisation algorithm and the effect of the proposed selective maintenance. The results show that the tailored ant colony optimisation algorithm is effective for the proposed selective maintenance model. This selective maintenance can significantly improve the system reliability when the predetermined period is relatively short, and this maintenance effect of improving the system reliability gradually weakens as the predetermined period becomes longer. Increasing the budget and decreasing the transportation volume requirement can slow this weakening of the maintenance effect.

For some engineering systems where there is a finite break between two consecutive missions, maintenance activities can only be performed during the break, and the budget is also limited. Due to limited resources, it is necessary to select a subset of the maintenance activities to be performed, with the aim of meeting the next mission requirement. This is termed as selective maintenance. We propose a selective maintenance model for a multi‐state system in which both maintenance task assignment and operating cost are considered. The algorithm of maintenance task assignment is proposed inspired by the 0–1 knapsack problem to help fulfill the maintenance optimization. The correctness and computationally effectiveness of this algorithm are validated. With the degradation of each component modeled as a homogenous Markov process, the operating cost of the system is modeled based on the Markov reward process and is treated as part of the total cost. Additionally, the randomness of the operating time is considered and the necessity of this consideration is illustrated. The proposed model is applied to a coal transportation system to demonstrate its effectiveness, and the effects of ignoring maintenance task assignment and operating cost are analyzed. The results show that it is essential to take the maintenance task assignment into consideration, especially when the time constraint plays a major limiting role, as failure to do so may lead to poor system optimization. Furthermore, not considering the operating cost will underestimate the requirement of the resources, more so when a higher reliability is required. [ABSTRACT FROM AUTHOR]

In order to improve the lifetime and reliability of equipment operation stage, it is necessary to carry out maintenance for components. The typical selective maintenance model does not consider the maintenance quality uncertainty and the failure effects, which may make the decision-maker overestimates the reliability of completing the next task after system maintenance, resulting in incorrect maintenance decision and prone to serious consequences of failure. In this paper, the potential discrepancy between the target maintenance quality and the actual maintenance quality of the decision maker is considered. At the same time, a multi-state FMECA is proposed to measure the failure effects under different states. Finally, the selective maintenance model of the multi-state series systems is established and solved by the genetic algorithm. The results show that considering the effect of component failure and the uncertainty of maintenance quality has an influence on maintenance decision. The maintenance decision made in this way is more consistent with the engineering practice. [ABSTRACT FROM AUTHOR]

Many systems are required to execute a series of missions with a finite break between any two adjacent missions. In order to improve the reliability of the system completing the next mission successfully, it is necessary to perform maintenance actions on the components considering limited maintenance resources. A novel idea to solve the problem of selective maintenance for a series-parallel system is proposed in this paper. This problem consists in finding the best choice of maintenance actions on a multi-component system by maximizing the system reliability under limited resources. With the increased number of components in the system, the possible combinations of selective maintenance increase exponentially, which raises the difficulty of the problem. For better solution of the problem, from the perspective of maintenance benefit, we develop a selective maintenance policy under maintenance priority of components. Finally, numerical example of a series-parallel system is given to illustrate the proposed method and verify the correctness and accuracy of the method.

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment's rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system. [ABSTRACT FROM AUTHOR]

Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty associated with the component's operational time, failure, and next mission duration create a new challenge in determining optimal components allocation and evaluating future missions successfully. In this paper, a multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment. A new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values. The neutrosophic goal programming technique is used to determine the compromise allocation of replaceable and repairable components based on the system reliability optimization. A numerical illustration is used to validate the model and ascertain its effectiveness. The result is compared with two other approaches and found to be better. The method is flexible and straightforward and can be solved using any available commercial packages. The extension of the concept can be useful to other complex system reliability optimization. [ABSTRACT FROM AUTHOR]

The flexibility of deployment strategies combined with the low cost of individual sensor nodes allow wireless sensor networks (WSNs) to be integrated into a variety of applications. Network operations degrade over time as sensors consume a finite power supply and begin to fail. In this work we address the selective maintenance of a WSN through a condition-based deployment policy (CBDP) in which sensors are deployed over a series of missions. The main contribution is a Markov decision process (MDP) model to maintain a reliable WSN with respect to region coverage. Due to the resulting high dimensional state and outcome space, we explore approximate dynamic programming (ADP) methodology in the search for high quality CBDPs. Our model is one of the first related to the selective maintenance of a large-scale WSN through the repeated deployment of new sensor nodes with a reliability objective, and one of the first ADP applications for the maintenance of a complex WSN. Additionally, our methodology incorporates a destruction spectrum reliability estimate which has received significant attention with respect to network reliability, but its value in a maintenance setting has not been widely explored. We conclude with a discussion on CBDPs in a range of test instances, and compare the performance to alternative deployment strategies. • Formulate a Markov Decision Process (MDP) model to redeploy nodes into a WSN. • Address selective maintenance of a large, complex network. • Propose an ADP algorithm customized using the destruction spectrum. • Demonstrate the model and solution procedure's efficacy through numerical examples. [ABSTRACT FROM AUTHOR]

Mission-oriented systems (MOS) are designed to operate a series of missions seceded by scheduled breaks of limited duration. For MOS, the most appropriate maintenance strategy is the selective maintenance (SM). Under limited maintenance resources, the SM problem (SMP) aims to identify the optimal set of components to maintain in order to meet the required minimum performance for the next missions. However, the majority of the existing SM models merely relies on stochastic independent (s -independent) components. To overcome this restrictive and unrealistic assumption, this work develops a novel SM approach for jointly optimizing SM and repairpersons assignment (RA) problem (RAP) in a MOS operating under s -dependent competing risks. The s -dependent competing risks between components are captured through copula functions whose unknown parameters are estimated by a two-stage maximum likelihood estimation (MLE) method. Furthermore, the MOS operate several missions and scheduled breaks, and multiple repair channels are available. The resulting joint SM and RAP (JSM-RAP) optimization model is formulated as a mixed-integer non-linear program (MINLP) where the objective is to minimize the total maintenance and labor costs for a SM plan that ensures a required minimum reliability threshold for each mission. A genetic algorithm (GA) is used as a solution method. Several numerical experiments are conducted on a ball screw system. The results obtained demonstrate the validity and the add value of the proposed approach, in addition to the robustness and efficiency of the solution method. • A selective imperfect maintenance and repairpersons assignment problem is proposed. • System's components are subjected to s -dependent competing risks. • Copula functions are used to capture components pairwise s-dependence. • Two-stage MLE is used for unknown parameters estimation. • The approach provides cost-effective solutions & accurate system's reliability. [ABSTRACT FROM AUTHOR]

• Stochastic programming for selective maintenance. • Uncertain mission time and operating conditions. • Expected failure and delay cost as a recourse. • SAA, EVPI, and VSS for solution accuracy under different sizes of scenarios. • Demonstrating the model effectiveness for different values of the break time. This paper studies selective maintenance for multi-component systems that undergo consecutive missions with scheduled breaks after each mission. To increase the likelihood of mission success, maintenance activities are performed on system components during the breaks. This study considers uncertainties in mission time and operating conditions. A two-stage stochastic programming approach is applied to model the uncertainties in the operating conditions of the next mission. Uncertainties in the operating conditions of the next mission affect the likelihood of successfully completing the mission, which may require reducing the mission time in worst-case scenarios. In the proposed two-stage model, the first stage involves making decisions on the maintenance actions required on selected components during the break. In the second stage, decisions are made regarding the completion or termination of the mission, and a penalty is assigned based on the probability of system failure during the next mission. The Sample Average Approximation algorithm, Wait-and-See, and Expected Value approaches are employed to demonstrate the efficiency of the optimal solution obtained from stochastic programming and to conduct large-scale analyses of the problem under various scenarios. Moreover, the effectiveness of the proposed model underscores the importance of incorporating uncertainty into the model. [ABSTRACT FROM AUTHOR]

Motivated by the need to reduce the maintenance cost and improve the system reliability, the issue of selective maintenance has attracted much attention in the industrial and military fields. The existing research mainly focused on binary-state systems or the perfect maintenance strategy. In this paper, we presented a novel selective maintenance model for multi-state deteriorating systems with multi-state components considering imperfect maintenance strategy. The proposed model aims to minimize the total maintenance costs and takes the relationship between the maintenance cost, the system service life and the maintenance quality into account. A case study where the proposed model was applied to an aircraft gas turbine engine system was conducted. The sensitivity analyses for the minimum requirement of the health level, the aging factor, and the number of health states were conducted, respectively. Moreover, a comparative analysis between perfect and imperfect maintenance strategies was conducted, and the results demonstrated that the imperfect maintenance strategy was more cost-effective than perfect maintenance strategy. The findings of this study can guide the maintenance decision-making process for actual systems.

Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.