This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. I...This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.展开更多
In view of the high complexity of the objective world, an economic dependence between subsystems(paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formul...In view of the high complexity of the objective world, an economic dependence between subsystems(paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formulated in a simple and consistent manner. Selective maintenance problem under economic dependence(EDSMP) is presented based on a series–parallel system in this paper. A case study shows that the system reliability is promoted to a certain extent, which can validate the validity of the EDSMP model. The influence of the ratio of set-up cost on system performance is mainly discussed under different economic dependences. Several existing improvements of classical exhaust algorithm are further modified to solve a large sized EDSMP rapidly. Experimental results illustrate that these improvements can reduce CPU time significantly.Furthermore the contribution of each improvement is defined here, and then their contributions are compared thoroughly.展开更多
The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-...The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-Copulabased reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs.Specifically,the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism.The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine.In addition,two RSM decision models are developed to ensure the system accomplishes the next mission.The genetic algorithm(GA)is used to solve the constraint optimization problem of the models.A numerical example illustrates the application of the proposed RSM method.展开更多
The selective maintenance is a new branch and significant breakthrough of reliability and maintenance theory.In the original selective maintenance problem,a subset of maintenance activities is performed on selected co...The selective maintenance is a new branch and significant breakthrough of reliability and maintenance theory.In the original selective maintenance problem,a subset of maintenance activities is performed on selected components during the finite break so that the system is able to maximize the next mission reliability.It is a fast growing research field over the past ten years,in which a variety of mathematical models and solution methodology have been proposed for dealing with more complex and significant problems.An overview of some recent advances in selective maintenance modeling and treatment methods is provided in this paper.A number of challenges that can be undertaken to help move the field forward are also discussed according to the current state of the selective maintenance approach.展开更多
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 su...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.展开更多
A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing...A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems.The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance,minimal repair and overhaul).In the meantime,a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided.Finally,a case study of the maintenance decision-making problem of automobile workshop was given.Furthermore,the case study demonstrates the practicability of this method.展开更多
Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy proce...Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.展开更多
The development of equipment maintenance management is introduced, and equipment maintenance concept is defined. Equipment maintenance modes are classified, analyzed and compared, which merits and demerits are pointed...The development of equipment maintenance management is introduced, and equipment maintenance concept is defined. Equipment maintenance modes are classified, analyzed and compared, which merits and demerits are pointed out. At last, a decision-making frame to select equipment maintenance modes is advanced, and steps to select and implement equipment maintenance are given.展开更多
Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distribu...Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity.The advent of Industry 4.0 has increased the use of sensors for monitoring systems,while deep learning(DL)models have allowed for accurate system health predictions,enabling data-driven maintenance planning.Most intelligent maintenance literature has used DL models solely for remaining useful life(RUL)point predictions,and a substantial gap exists in further using predictions to inform maintenance plan optimization.The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models.Hence,this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem(SMP).The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system.Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths.When minimizing cost,maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs.The proposed method is usable in large-scale,complex scenarios and various industrial contexts.The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.展开更多
In many industrial settings,fleets of assets are required to operate through alternating missions and breaks.Fleet Selective Maintenance(FSM)is widely used in such contexts to improve the fleet performance.However,exi...In many industrial settings,fleets of assets are required to operate through alternating missions and breaks.Fleet Selective Maintenance(FSM)is widely used in such contexts to improve the fleet performance.However,existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion.Additionally,these models typically assume that all missions must be completed,overlooking resource constraints that may prevent readying all systems within the available break duration.This makes mission prioritization and assignment a necessary consideration for the decision-maker.This work proposes a novel FSM model that jointly optimizes system to mission assignment,component and maintenance level selection,and repair task allocation.The proposed framework integrates analytical models for standard components and Deep Neural Networks(DNNs)for sensor-monitored ones,enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems.To account for uncertainties in maintenance and break durations,a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level.The optimization model is reformulated using two well-known techniques:Sample Average Approximation(SAA)and Conditional Value-at-Risk(CVaR)approximation.A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.展开更多
文摘This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.
基金supported by the National Science Foundation of China (Grant No. 61305083)
文摘In view of the high complexity of the objective world, an economic dependence between subsystems(paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formulated in a simple and consistent manner. Selective maintenance problem under economic dependence(EDSMP) is presented based on a series–parallel system in this paper. A case study shows that the system reliability is promoted to a certain extent, which can validate the validity of the EDSMP model. The influence of the ratio of set-up cost on system performance is mainly discussed under different economic dependences. Several existing improvements of classical exhaust algorithm are further modified to solve a large sized EDSMP rapidly. Experimental results illustrate that these improvements can reduce CPU time significantly.Furthermore the contribution of each improvement is defined here, and then their contributions are compared thoroughly.
基金supported by the Aeronautical Science Foundation of China(20150863003).
文摘The reliability-based selective maintenance(RSM)decision problem of systems with components that have multiple dependent performance characteristics(PCs)reflecting degradation states is addressed in this paper.A vine-Copulabased reliability evaluation method is proposed to estimate the reliability of system components with multiple PCs.Specifically,the marginal degradation reliability of each PC is built by using the Wiener stochastic process based on the PC’s degradation mechanism.The joint degradation reliability of the component with multiple PCs is established by connecting the marginal reliability of PCs using D-vine.In addition,two RSM decision models are developed to ensure the system accomplishes the next mission.The genetic algorithm(GA)is used to solve the constraint optimization problem of the models.A numerical example illustrates the application of the proposed RSM method.
基金Project of Science Research Plan in Xi'an of China(No.CXY1439(8))National Natural Science Foundations of China(Nos.61100009,61305083)
文摘The selective maintenance is a new branch and significant breakthrough of reliability and maintenance theory.In the original selective maintenance problem,a subset of maintenance activities is performed on selected components during the finite break so that the system is able to maximize the next mission reliability.It is a fast growing research field over the past ten years,in which a variety of mathematical models and solution methodology have been proposed for dealing with more complex and significant problems.An overview of some recent advances in selective maintenance modeling and treatment methods is provided in this paper.A number of challenges that can be undertaken to help move the field forward are also discussed according to the current state of the selective maintenance approach.
文摘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.
基金Project(51105141,51275191)supported by the National Natural Science Foundation of ChinaProject(2009AA043301)supported by the National High Technology Research and Development Program of ChinaProject(2012TS073)supported by the Fundamental Research Funds for the Central University of HUST,China
文摘A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems.The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance,minimal repair and overhaul).In the meantime,a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided.Finally,a case study of the maintenance decision-making problem of automobile workshop was given.Furthermore,the case study demonstrates the practicability of this method.
基金the Weapons and Equipment Preresearch Fund(No.9140A27040414JB34079)the Specialized Research Fund for the Doctoral Program of the Military Education(No.2015JY354)
文摘Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.
基金This paper is sponsored by Natural Science Fund of Shenyang Municipality under Grant No.1041007104 and Doctor Fund of Liaoning Province under Grant No.L050517.
文摘The development of equipment maintenance management is introduced, and equipment maintenance concept is defined. Equipment maintenance modes are classified, analyzed and compared, which merits and demerits are pointed out. At last, a decision-making frame to select equipment maintenance modes is advanced, and steps to select and implement equipment maintenance are given.
文摘Modern industries dependent on reliable asset operation under constrained resources employ intelligent maintenance methods to maximize efficiency.However,classical maintenance methods rely on assumed lifetime distributions and suffer from estimation errors and computational complexity.The advent of Industry 4.0 has increased the use of sensors for monitoring systems,while deep learning(DL)models have allowed for accurate system health predictions,enabling data-driven maintenance planning.Most intelligent maintenance literature has used DL models solely for remaining useful life(RUL)point predictions,and a substantial gap exists in further using predictions to inform maintenance plan optimization.The few existing studies that have attempted to bridge this gap suffer from having used simple system configurations and non-scalable models.Hence,this paper develops a hybrid DL model using Monte Carlo dropout to generate RUL predictions which are used to construct empirical system reliability functions used for the optimization of the selective maintenance problem(SMP).The proposed framework is used to plan maintenance for a mission-oriented series k-out-of-n:G system.Numerical experiments compare the framework’s performance against prior SMP methods and highlight its strengths.When minimizing cost,maintenance plans are frequently produced that result in mission survival while avoiding unnecessary repairs.The proposed method is usable in large-scale,complex scenarios and various industrial contexts.The method finds exact solutions while avoiding the need for computationally-intensive parametric reliability functions.
基金funded by the Natural Sciences and Engineering Research Council of Canada.
文摘In many industrial settings,fleets of assets are required to operate through alternating missions and breaks.Fleet Selective Maintenance(FSM)is widely used in such contexts to improve the fleet performance.However,existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion.Additionally,these models typically assume that all missions must be completed,overlooking resource constraints that may prevent readying all systems within the available break duration.This makes mission prioritization and assignment a necessary consideration for the decision-maker.This work proposes a novel FSM model that jointly optimizes system to mission assignment,component and maintenance level selection,and repair task allocation.The proposed framework integrates analytical models for standard components and Deep Neural Networks(DNNs)for sensor-monitored ones,enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems.To account for uncertainties in maintenance and break durations,a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level.The optimization model is reformulated using two well-known techniques:Sample Average Approximation(SAA)and Conditional Value-at-Risk(CVaR)approximation.A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.
