Selective Maintenance Model Research Articles

Robust selective maintenance strategy under imperfect observations: A multi-objective perspective

Selective maintenance, as a pervasive maintenance policy in both military and industrial environments, aims to achieve the maximum success of subsequent missions under limited maintenance resources by choosing an optimal subset of feasible maintenance actions. The existing works on selective maintenance optimization all assume that the condition of components in a system can be perfectly observed after the system completes the last mission. However, such a premise may not always be true in reality due to the limited accuracy/precision of sensors or inspection instruments. To fill this gap, a new robust selective maintenance model is proposed in this work to consider uncertainties that originate from imperfect observations. The uncertainties associated with imperfect observations are incorporated into the states and effective ages of components via Bayes rule. The Kijima type II model, as a specific imperfect maintenance model, is used to characterize the imperfect maintenance efficiency of each selected maintenance action. The expectation and variance of the probability of a repairable system successfully completing the subsequent mission are derived to quantify the uncertainty that is propagated from imperfect observations. To guarantee the robustness of a selective maintenance strategy under uncertainties, a multi-objective selective maintenance model is constructed with the aims of maximizing the expectation of the probability that a system successfully completes the subsequent mission and to simultaneously minimizing the variance in this probability. The Pareto-optimality approach is utilized to offer a set of non-dominated solutions. Two illustrative examples are presented to demonstrate the advantages of the proposed method.

Selective maintenance strategy for systems executing multiple consecutive missions with uncertainty

Selective maintenance is extensively implemented in industrial and military environments. With it, a subset of feasible maintenance actions may be strategically selected for a repairable system to achieve maximum success in subsequent missions with limited maintenance resources. In this study, a new selective maintenance model for systems that execute multiple consecutive missions is developed. In each break, multiple optional maintenance actions, from perfect replacements down to imperfect and minimal repairs, can be chosen for each component. Because of the uncertainties associated with the operation time of each component and the durations of future missions, the effective age of each component at the beginning of the next mission is also uncertain, posing a new challenge in terms of evaluating the success of subsequent missions. Such uncertainties are quantified by evaluating the probabilities of a system in successfully completing future missions. The computational burden resulting from the use of multi-dimensional integrals is alleviated with the introduction of the Gaussian quadrature and Riemann sum. Consequently, the selective maintenance problem is formulated as a max-min optimization model. Moreover, the simulated annealing-based genetic algorithm is customized to solve the resulting optimization problem. Two illustrative examples are presented to demonstrate the advantages of the proposed approach.

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.

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.

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 optimisation considering random common cause failures and imperfect maintenance

This article addresses a selective maintenance optimisation problem for systems subject to random common cause failures. A system is likely to suffer from several random common cause failures during a given mission. Random common cause events, which occur with a specific probability distribution, may result in the simultaneous failures of multiple elements. Because time is one of the most crucial maintenance resources, a time-based imperfect maintenance model is proposed to quantify the maintenance efficiency of each candidate maintenance action. To meet the demands of the next mission, a selective maintenance model is proposed to optimally identify a subset of maintenance activities to be performed on certain elements of a system. A genetic algorithm and Monte Carlo simulation method is presented to solve the proposed selective maintenance optimisation problem. Illustrative examples combined with detailed discussions are presented to demonstrate the effectiveness of the proposed model. The results show that the proposal of time-based imperfect maintenance model can yield better maintenance results, while ignoring random common cause failures in selective maintenance optimisation may produce biased maintenance decisions and system reliability.

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.

Optimal joint selective imperfect maintenance and multiple repairpersons assignment strategy for complex multicomponent systems

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.

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.

Fleet-level selective maintenance problem under a phased mission scheme with short breaks: A heuristic sequential game approach

Abstract Selective maintenance is the most widely used strategy for identifying and performing the maintenance actions necessary for fleet mission success. A fleet of equipment is usually required to perform phased missions with short scheduled breaks. In this case, a selective maintenance model should be extended for frequency-based maintenance optimization. We research the problem considering the application of condition-based maintenance (CBM). The problem is formulated with the objective of reducing the repair frequency and cost. The constraint is the reliability of the phased mission, and the variables are the remaining useful lifetimes (RUL) of all the key subsystems. The equipment can be classified into three echelons based on the health status before each wave of a mission, and a heuristic game framework with state backtracking is proposed for the three echelons to solve the problem. The flowchart and heuristic rules of the game framework are given, and the game algorithms for the second and third echelons are presented. The second echelon algorithm aims to select the dispatched equipment for the current wave and minimize maintenance, and the third echelon algorithm aims to ensure that sufficient equipment is available for the next wave by performing necessary maintenance. Finally, we present two types of strategy adjustment methods with state backtracking to turn infeasible solutions into feasible solutions and to optimize feasible solutions. To verify the capacity of the proposed method, a case involving a fleet of 12 aircraft is analyzed for a three-mission scheme, and the aircraft repair times and costs are reduced by the method.

Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.

On sequence planning for selective maintenance of multi-state systems under stochastic maintenance durations

In many industrial and military environments, systems are required to execute a sequence of missions with a finite break between two adjacent missions. Due to the limited maintenance resources, such as budget, time, and manpower, etc., it is oftentimes impossible to perform all the desirable maintenance actions in the break. In such circumstance, selective maintenance is able to identify a subset of feasible maintenance actions to be conducted, so as to guarantee the success of the next mission. In this paper, a new selective maintenance model for multi-state systems is developed to maximize the probability of a system successfully completing the next mission, while taking account of the stochasticity of the durations of breaks and maintenance actions. Due to the presence of these duration uncertainties, it necessitates (1) choosing a subset of maintenance actions from all the optional maintenance actions, and (2) planning a sequence of selected maintenance actions to be performed. The saddlepoint approximation is utilized to facilitate the computation of the involved multi-dimensional integration in evaluating the probability of a system successfully completing the next mission. The resulting constrained combinational optimization problem is resolved by a tailored ant colony optimization algorithm. A numerical example of a three-unit multi-state system, together with an illustrative example of a multi-state coal transportation system, is presented to examine the effectiveness of the proposed method.

Selective Maintenance under Uncertainty

In this paper, a new multi-objective robust selective maintenance model is formulated by considering the uncertainties produced by imperfect inspections. The resulting optimization problem aims at maximizing the expectation of the probability of a repaired system completing a mission and simultaneously minimizing its variance. A multi-objective particle swarm optimization algorithm in introduced to identify the Pareto front, which offer a set of non-dominated selective maintenance strategies. An illustrative example shows that the selective maintenance strategy with the maximum expectation of the probability of a repaired system completing a mission may not be desirable as it usually possesses a huge variance. Several comparative studies are also conducted to examine the effect of observation accuracy and maintenance budget on the results. It concludes that the proposed approach can effectively improve the robustness of the probability of a repaired system completing a mission.

Selective Maintenance for Multistate Series Systems With S-Dependent Components

In this paper, we will consider the selective maintenance problem for multistate series systems with stochastic dependent components. In multistate systems, the health state of a component may vary from perfect functioning to complete failure. The stochastic dependence (S-dependence) between components is discussed and categorized into two types in multistate context. First, the failure of a component can immediately cause complete failures of some other components in the system. Second, as components deteriorate, the reduced working performance rate of a multistate component affects the state as well as the degradation rate of its subsequent components in series structure. The system reliability is evaluated using an approach based on stochastic process. A cost-based selective maintenance model is developed for the multistate system with S-dependent components to maximize the total system profit, which includes the production gain and loss in the next mission as well as possible maintenance costs for the system. Analyses of systems with independent and dependent components are provided. It is observed that ignoring S-dependence in the system may lead to alternative maintenance decision making and an optimistic estimation of the system performance.

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.

Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance

In many industrial environments, maintenance is performed during successive mission breaks. In these conditions, it may not be feasible to perform all possible maintenance actions due to limited maintenance resources such as time, budget, repairman availability, etc. A subset of maintenance actions is then performed on selected components such that the system is able to meet the next mission requirement. Such a maintenance policy is called selective maintenance. In this article, a selective maintenance strategy is developed for a MultiState System (MSS). The system can have several finite levels of performance in an MSS. Previous studies on selective maintenance have solely focused on MSSs with binary components. However, components in an MSS may be in more than two possible states. Hence, a series-parallel MSS that consists of multistate components is considered in this article. Imperfect maintenance of a component is considered to be a maintenance option, along with the replacement and the do-nothing options. Maintenance resources need to be allocated such that maximum system reliability during the next mission is ensured. A universal generating function is used to determine system reliability. An illustrative example is presented that depicts the advantages of utilizing imperfect maintenance/repair options.

Selective maintenance for binary systems under imperfect repair

In many industrial environments like manufacturing systems, military equipments, power generation systems, etc., system maintenance is performed between successive missions. Different maintenance options (do nothing, minimal repair, preventive maintenance options or system overhaul, etc.) are possible for components in the system. However, it may not be feasible to do all possible maintenance actions during the maintenance break. Hence, optimal maintenance decision is required such that available resources are optimally used to maximize the next mission reliability. In this paper, a mathematical model is used to help in decision making for selective maintenance under imperfect repair. The level of maintenance actions determines the improvement in the component health. A model is formulated to relate the amount of resources used for maintenance to the level of imperfect repair. Further, a characteristic constant is used which determines the component response to resource consumed by a maintenance task. Selective maintenance model is formulated and illustrative examples are used to demonstrate the applicability and advantages of the proposed method. The results show that introduction of imperfect repair facilitates better allocation of maintenance resources.

A cost‐based selective maintenance decision‐making method for machining line

AbstractMachine line is a type of manufacturing system in which machines are connected in series or in parallel. It is significant to ensure the reliability as well as to reduce the total cost of maintenance and failure losses in the maintenance programs of such systems. Cost‐based selective maintenance decision‐making, which is the best method for a selected group of machines in machine line is presented under limited maintenance durations. Fault losses and maintenance costs of a single machine under different maintenance actions i.e. minimal repair, preventive maintenance and overhaul on the fault rate of the machine are calculated. An algorithm combining the heuristic rules and tabu search is proposed to solve the presented selective maintenance model. Finally, a case study on the maintenance decision‐making problem of a connecting rod machining line in the automobile engine workshop is presented to illustrate the applicability of the proposed method. The end result shows that the fault losses can be further reduced by the optimization of maintenance interval and maintenance duration. Copyright © 2010 John Wiley & Sons, Ltd.

Selective Maintenance Decision-Making Over Extended Planning Horizons

¿Selective maintenance¿ models determine the optimal subset of desirable maintenance actions to perform when maintenance resources are constrained. We analyse a corrective selective maintenance model that identifies which components to replace in the finitely long periods of time between missions performed by a series-parallel system. We formulate this multi-mission problem as a stochastic dynamic program, and compare the resulting optimal infinite-horizon policy to both the optimal single-mission, and two-mission policies by executing a large numerical experiment. Our results indicate that these policies rarely differ, and that when they do, the difference in long-run mission reliability is minimal, which suggests that future work should concentrate on extending results for the single-mission problem.

Selective maintenance modeling for industrial systems

In many industrial environments, systems are required to perform a sequence of operations (or missions) with finite breaks between each operation. During these breaks, it may be advantageous to perform repair on some of the system’s components. However, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission due to limitations on maintenance resources. In this paper, a mathematical programming framework is established for assisting decision‐makers in determining the optimal subset of maintenance activities to perform prior to beginning the next mission. This decision‐making process is referred to as selective maintenance. The selective maintenance models presented allow the decision‐maker to consider limitations on maintenance time and budget, as well as the reliability of the system. Selective maintenance is an open research area that is consistent with the modern industrial objective of performing more intelligent and efficient maintenance.