Selective Maintenance Optimization Research Articles

Multi-mission selective maintenance modelling for multistate systems over a finite time horizon

In the life cycle of a long-life structure, multiple maintenances are often required to ensure safety or normal performance. This article developed a multi-mission selective maintenance optimization model for a system with several multistate components over a finite time horizon. The model aims to minimize the total cost by determining the optimal intervention time and the corresponding maintenance option of each component. The system reliability requirement is taken as the constraint. The degradation process of each component is characterized by a homogeneous continuous time Markov chain in this model. Considering the uncertainties that exist during the deterioration–maintenance process, the condition state of each component at any time is described by a state probability vector. In addition, the components in the system are considered to be structurally dependent during the maintenance process, and a directed graph is used to represent the precedence relations among these components. A differential evolution algorithm is utilized to solve this complicated optimization model. A numerical example and an engineering application are presented to illustrate the availability of the proposed model.

Selective Maintenance Optimization for a Multi-State System Considering Human Reliability

In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS.

Mission Reliability-Oriented Selective Maintenance Optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

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.

Selective Maintenance Optimization for a Multi-State System With Degradation Interaction

This paper studies the selective maintenance problem for a multi-state system (MSS) performing consecutive production missions with scheduled intermission breaks. To improve the reliability of the system successfully performing the next mission, all maintenance actions need to be carried out during maintenance breaks. However, it may not be feasible to repair all components due to the limited maintenance resources (such as time, costs, and manpower). Hence, a selective maintenance model was established to identify a subset of maintenance actions to perform on the repairable components. We extend the original model in several ways. First, we consider the role of degradation interaction in determining the state transition probability of each component. Back-propagation (BP) neural network is employed to predict the transition matrix since it is not practicable to analyze the degradation processes of all components using the traditional probability model. Second, a selective maintenance optimization model for an MSS is established based on the prediction results of the BP neural network and solved by a genetic algorithm (GA). Finally, an example is illustrated to verify the effectiveness and superiority of the proposed method.

Joint optimization of the selective maintenance and repairperson assignment problem when using new and remanufactured spare parts

This paper deals with the problem of the selective maintenance (SM) optimization for a series-parallel system. The system performs several missions with breaks between consecutive missions. To improve the system reliability during the next mission, its components are maintained during the breaks. Current models in the SM literature usually assume that when a component is subjected to a replacement, it is done by a new one. This paper introduces a novel variant of the selective maintenance problem (SMP) where a mixture of new and reconditioned/remanufactured parts are used to carry out replacements. It has indeed been proved that remanufacturing processes can extend the life of a product returned from the field. This provides not only economic opportunities but also favours sustainable practices. Accordingly, a novel mixed integer nonlinear programming model of the SMP is developed and optimally solved. Numerical experiments show how using reconditioned spare parts impacts the SM decisions.

Developing a bi-objective imperfect selective maintenance optimization model for multicomponent systems

This paper develops a bi-objective imperfect selective maintenance optimization model for a multicomponent system, which carries out missions interspersed with scheduled breaks. Imperfect maintenance (IM) actions are performed on the components during the break to increase the system reliability during the following mission. The level of maintenance performed determines the improvement of the component’s health. A mathematical model with two objective functions is developed to optimize the tradeoffs between the total maintenance cost and the system reliability based on the decision maker’s preferences. Numerical examples are provided to show that the proposed model reaches valid maintenance decisions. Furthermore, it is shown that when high system reliability is required, the optimal decision is not significantly affected by the decision-maker’s preference for one objective or the other.

Selective Maintenance Optimization Modelling for Multi-State Deterioration Systems Considering Imperfect Maintenance

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 optimization for random phased-mission systems subject to random common cause failures

In both industrial and military fields, there is such a kind of complicated system termed as phased-mission system, which executes missions composed of several different phases in sequence. The structure, failure behavior, and working conditions of such a system may change from phase to phase. The duration of each phase of such a system involved is random and follows a probability distribution, and the system may suffer some events resulting in simultaneous failures of different elements with different probabilities. In order to guarantee such a system completes the phased-mission successfully, a selective maintenance model for random phased-mission systems subject to random common cause failures is proposed to optimally identify a subset of maintenance activities to be performed on some elements of the system. Thereinto, a novel analytic model is developed to estimate the probability of the maintained random phased-mission system successfully completing the phased-mission, and we compare it with a well-known Monte Carlo Simulation approach. Finally, the proposed selective maintenance model has been successfully applied to an artillery weapon system. Comparative analysis is carried out to compare the proposed model with the traditional ones, including selective maintenance models for deterministic phased-mission systems and deterministic single-phase mission systems. The results show that ignoring some mission properties (e.g. randomness and multiple phases) in selective maintenance optimization will lead to (1) incorrect system and mission modeling, (2) incorrect computation of the probability of the random phased-mission system successfully completing a mission, and/or (3) nonoptimal selective maintenance options.

Condition-based selective maintenance for stochastically degrading multi-component systems under periodic inspection and imperfect maintenance

This article proposes a novel condition-based selective maintenance model for a multi-component system running multiple missions interspersed with scheduled intermission breaks. Each component in the system degrades according to a time-dependent stochastic process and fails whenever its degradation level reaches a prespecified threshold. Failures of system components are revealed only through periodic inspections performed during a mission. The decision to repair components found in a failed state is made at the beginning of the following break. However, a penalty cost proportional to the expected component downtime is incurred. To improve the probability of the system successfully completing its next mission, maintenance activities are carried out on its components during the breaks. Each component can be imperfectly maintained or replaced. The level at which maintenance is performed determines the improvement degree in the component health. Cost and time structures are developed to take into account the trade-offs between the cost of an imperfect maintenance action and its resulting health improvement. Given the limited duration of the break and the required reliability target for the next mission, the condition-based selective maintenance problem aims at finding an optimal subset of maintenance actions to be performed on the selected components to minimize the total expected cost which is the sum of the total expected maintenance, inspection and penalty costs. All parameters and components of this nonlinear selective maintenance optimization problem are developed and thoroughly discussed. Numerical experiments are provided to illustrate the modelling steps and show the validity of the proposed approach.

A literature review on selective maintenance for multi‐unit systems

AbstractSelective maintenance is regarded as a type of profit‐generating maintenance policy, playing an important role in balancing limited maintenance resources with system performance. Since 1988, increasing interest has been focused on this research area. Nevertheless, to the best of our knowledge, there is a lack of critical reviews of selective maintenance. This paper is the first systematic review focusing on this relevant topic. In this work, a definition and some specific features of selective maintenance are elaborated. Based on these features, a set of criteria that have been considered in selective maintenance optimization are summarized into 3 categories: system characteristics, maintenance characteristics, and mission profile characteristics. Based on these criteria, a comprehensive literature review on selective maintenance is undertaken. The solution approaches, as well as a general procedure for selective maintenance optimization, are discussed. Finally, some possible directions for further research are provided.

Selective maintenance optimization for fuzzy multi-state systems

Selective maintenance optimization for fuzzy multi-state systems

Selective maintenance optimization for systems operating missions and scheduled breaks with stochastic durations

This paper deals with selective maintenance of a multi-component system, performing several missions seceded by scheduled breaks. To improve the probability of successfully completing its next mission, the system's components are maintained during these breaks. A list of possible maintenance actions on each component of the system, ranging from minimal repair to overhaul through intermediate imperfect maintenance actions, is available. Durations of the missions as well as those of the breaks are assumed stochastic variables with known distributions. The resulting selective maintenance optimization problem is thus modeled as a mixed-integer non-linear stochastic program. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the system's components, during the limited stochastic duration of the break, to meet a predetermined minimum system's reliability to operate the next mission. The fundamental constructs and the relevant parameters of this decision-making problem are developed and discussed. An illustrative example is provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimization program.

Selective maintenance of multi-state systems with structural dependence

This paper studies the selective maintenance problem for multi-state systems with structural dependence. Each component can be in one of multiple working levels and several maintenance actions are possible to a component in a maintenance break. The components structurally form multiple hierarchical levels and dependence groups. A directed graph is used to represent the precedence relations of components in the system. A selective maintenance optimization model is developed to maximize the system reliability in the next mission under time and cost constraints. A backward search algorithm is used to determine the assembly sequence for a selective maintenance scenario. The maintenance model helps maintenance managers in determining the best combination of maintenance activities to maximize the probability of successfully completing the next mission. Examples showing the use of the proposed method are presented.

Selective maintenance optimization under schedule and quality constraints

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Practical implications – 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. Originality/value – 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.

Selective maintenance optimization when quality of imperfect maintenance actions are stochastic

This paper addresses the selective maintenance optimization problem in a multi-component system, carrying out several missions with scheduled inter-mission breaks. To improve the probability of the system successfully completing the next mission, maintenance is performed on the system׳s components during the break. Each component is assigned a list of eligible maintenance actions ranging from minimal repair, through intermediate imperfect maintenance actions, to replacement. The quality of a maintenance action is assumed to be stochastic, reflecting the degree of expertise of the repairman and the tools used to perform the maintenance action. This quality is thus treated as a random variable with an identified probability distribution. The selective maintenance problem aims thus at finding a cost-optimal subset of maintenance actions, to be performed on the system during the limited duration of the break, which guarantees that the pre-set minimum probability of successfully completing the next mission is attained. The fundamental constructs and the relevant parameters of this nonlinear and stochastic optimization problem are developed and thoroughly discussed. It is then put into practice for a series–parallel system and the added value of solving it as a stochastic problem is demonstrated on some test cases.

Selective maintenance for series-parallel systems when durations of missions and planned breaks are stochastic

This paper addresses the selective maintenance optimization problem for a series-parallel system. The system performs several missions with breaks between each two consecutive missions. To improve the system reliability to operate the next mission, its components are maintained during breaks. To each component, a list of maintenance actions is available from minimal repair to overhaul through imperfect maintenance actions. Durations of missions as well as that of breaks are considered not constant but rather stochastic. These durations are therefore modeled as random variables. The selective maintenance optimization problem developed is then non-linear and stochastic. The fundamental constructs and the relevant parameters of this decision-making problem are developed and discussed. A simplified series-parallel system is investigated to demonstrate the added value of solving this non-linear and stochastic selective maintenance optimization program.

Selective maintenance optimization for series-parallel systems with continuously monitored stochastic degrading components subject to imperfect maintenance

Abstract: This paper addresses a degradation-based selective maintenance decision problem in a continuously monitored multi-component system, carrying out several missions with scheduled inter-mission breaks. Each system’s component is modeled as a time-dependent stochastic process. Each component is also assigned a list of maintenance actions ranging from imperfect maintenance actions up to complete overhauling. Both corrective and preventive imperfect maintenance are allowed. To improve the probability of the system successfully completing the next mission, maintenance is performed on the system’s components during the break. The selective maintenance problem aims thus at finding a cost-optimal subset of maintenance actions, to be performed on the system during the limited duration of the break, which guarantees the successful completion of the next mission with a minimum reliability level. The fundamental constructs and the relevant parameters of this new nonlinear selective maintenance optimization problem are developed and thoroughly discussed. A numerical example is provided to illustrate the proposed approach.

Evaluation and comparison of alternative fleet-level selective maintenance models

Fleet-level selective maintenance refers to the process of identifying the subset of maintenance actions to perform on a fleet of repairable systems when the maintenance resources allocated to the fleet are insufficient for performing all desirable maintenance actions. The original fleet-level selective maintenance model is designed to maximize the probability that all missions in a future set are completed successfully. We extend this model in several ways. First, we consider a cost-based optimization model and show that a special case of this model maximizes the expected value of the number of successful missions in the future set. We also consider the situation in which one or more of the future missions may be canceled. These models and the original fleet-level selective maintenance optimization models are nonlinear. Therefore, we also consider an alternative model in which the objective function can be linearized. We show that the alternative model is a good approximation to the other models.

Optimal selective renewal policy for systems subject to propagated failures with global effect and failure isolation phenomena

This paper considers a selective maintenance policy for multi-component systems for which a minimum level of reliability is required for each mission. Such systems need to be maintained between consecutive missions. The proposed strategy aims at selecting the components to be maintained (renewed) after the completion of each mission such that a required reliability level is warranted up to the next stop with the minimum cost, taking into account the time period allotted for maintenance between missions and the possibility to extend it while paying a penalty cost. This strategy is applied to binary-state systems subject to propagated failures with global effect, and failure isolation phenomena. A set of rules to reduce the solutions space for such complex systems is developed. A numerical example is presented to illustrate the modeling approach and the use of the reduction rules. Finally, the Monte-Carlo simulation is used in combination with the selective maintenance optimization model to deal with a number of successive missions.

Optimal Selective Maintenance Strategy for Multi-State Systems Under Imperfect Maintenance

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. In such a case, one of the most widely used maintenance policies is a selective maintenance in which a subset of feasible maintenance actions is chosen to be performed with the aim at achieving the subsequent mission success under limited maintenance resources. Traditional selective maintenance optimization reported in the literature only focuses on binary state systems. Most systems in industrial applications, however, have more than two states in the deterioration process. In this work, a selective maintenance policy for multi-state systems (MSS) consisting of binary state elements is investigated. Taking the imperfect maintenance quality into consideration, the Kijima model is reviewed, and a cost-maintenance quality relationship which considers the age reduction factor as a function in terms of maintenance cost is established. Moreover, with the assistance of the universal generating function (UGF) method, the probability of the repaired MSS successfully completing the subsequent mission is formulated. In place of enumerative methods, a genetic algorithm (GA) is employed to solve the complicated optimization problem where both multi-state systems, and imperfect maintenance models are taken into account. The effectiveness of the proposed method is demonstrated via a case study of a power station coal transportation system. Finally, a comparative analysis between the strategies with and without considering imperfect maintenance is conducted, and it is concluded that incorporating imperfect maintenance quality into selective maintenance achieves better outcomes.