Selective Maintenance Model Research Articles

Selective maintenance of the complex system considering maintenance time uncertainty for system components with multiple repairpersons

AbstractThis research presents an innovative selected maintenance model for complex systems that considers the uncertainty in maintenance time (MT) for system components with multiple repairpersons. The computational model of uncertain MT for system components is established. An imperfect maintenance model is introduced, which has many imperfect maintenance levels not only considering do nothing, minimal repair, and replacement but also considering multiple intermediate maintenance levels. Furthermore, the system components maintenance assignment algorithm with multiple repairpersons is proposed to addresses the problem of how to assign multiple maintenance tasks to multiple repairpersons in order to minimize system MT. And it is innovatively integrated into the particle swarm optimization (PSO) to solve the proposed selective maintenance model, which enables heuristic algorithm to efficiently assign the multiple maintenance tasks with multiple repairpersons. The effectiveness and advantages of the proposed model and algorithm are verified by numerical experiments.

A multi-stage stochastic programming model for multi-mission selective maintenance optimization

This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each mission. Within each maintenance break, various optional actions ranging from replacements which are perfect to imperfect and also minimal repairs can be chosen for individual components. Evaluating the probabilities of successful future mission accounts for uncertainties associated with component operational conditions. The selective maintenance problem is formulated as a nonlinear mixed-integer model for optimization, and computational challenges are addressed using the progressive hedging algorithm. Numerical examples validate the new proposed model and illustrate the benefits of the model by estimating a more realistic reliability level and lower maintenance cost.

An optimal selective maintenance policy for multi-state series production systems by considering environment impact

Series production systems are prevalent in the manufacturing industry. The productivity of these systems is influenced by various factors, including system conditions, on-site environment, maintenance planning, and others. Efforts to develop an effective maintenance plan that considers the on-site environment are crucial for maximizing net revenue over the system’s lifetime, which has practical significance. Against this backdrop, a selective maintenance model is constructed for a multi-state series production system over a finite time horizon. This model describes the operation and maintenance processes at any decision period and analyzes the impact of the on-site environment on system productivity. Subsequently, an optimal maintenance strategy is formulated with the goal of maximizing net revenue throughout the system’s lifetime. To efficiently solve this optimal strategy, a hybrid solution approach is developed by integrating a deterministic method with a heuristic method. Finally, a numerical example involving a real chip mounter system is presented to verify the developed maintenance strategy. This is followed by a comparative experiment to validate the performance of the hybrid solution approach. Additionally, sensitivity analysis is conducted to test the strategy’s robustness. The results from the numerical example, comparative experiment, and sensitivity analysis demonstrate that the developed strategy is effective and robust, and the hybrid approach is efficient.

Optimization of selective maintenance problem with stochastic durations in mission-oriented system subjecting to [formula omitted]-dependent competing risks

The mission-oriented systems (MOS) are often required to operate a consecutive of missions interspersed with finite breaks. For MOS, an appropriate maintenance strategy is the selective maintenance (SM). The SM problem (SMP) aims then to identify an optimal set of components to be maintained under maintenance resource constraints such as limited break durations. However, most existing SM models merely deals with either stochastic independent (s-independent) components in MOS or deterministic mission and break durations. To overcome this unrealistic restriction, this paper develops a novel SM model for optimizing the SMP in MOS operating under stochastic mission and break durations and s-dependent competing risks (CRs). Durations of missions and breaks are respectively governed by normal and uniform distributions, while the s-dependent CRs are captured by the copula function of which the unknown parameters are estimated relying on the two-stage MLE method. Furthermore, due to the stochastic break durations, an integrated importance measure (IIM) is introduced to measure the maintenance priority on selected components. The resulting SM optimization model is therefore formulated as a mixed-integer nonlinear program (MINLP) whose objective is to minimize the total cost while ensuring the required minimum mission reliability. A differential evolution (DE) algorithm is designed to solve the MINLP. Two numerical experiments are conducted on a ball screw system. Results obtained demonstrate the validity of our proposed model, in addition to the robustness of the solution algorithm designed.

Joint optimization of selective maintenance and repairpersons assignment problem for mission-oriented systems operating under [formula omitted]-dependent competing risks

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.

Mission and Reliability Driven Fleet-Level Selective Maintenance Planning and Scheduling Two-Stage Method

This paper studies the problem of planning and scheduling in selective maintenance tasks of mission requirements and the health condition of the fleet. In order to deal with the problems of high maintenance cost and long time consumption in maintenance systems, a two-stage fleet maintenance optimization method is proposed. Firstly, a selective maintenance model of fleets based on age reduction is established to maximize the probability of completing the next mission and minimize the maintenance cost. Secondly, a multiobjective sparrow search algorithm is designed to solve the maintenance planning problem in the first stage, and a nondominated solution set of maintenance strategies satisfying the mission constraint is obtained. In the second stage, the simulated annealing algorithm is used to schedule the maintenance task and obtain the minimum maintenance hours required by the maintenance strategy. An example analysis of a vehicle fleet is launched to prove the effectiveness of this method. In a word, this method not only meets the mission requirements but also achieves the purpose of reducing maintenance cost and maintenance hours, which can provide reference for other types of equipment maintenance.

Optimizing system reliability through selective maintenance allocation: A novel multi‐objective programming approach using neutrosophic fuzzy concept

AbstractSelective maintenance refers to the problem of decision‐making on how to maintain a multi‐component complex system that can be repaired or replaced. The goal is generally to plan ahead in order to improve the likelihood of success for future maintenance tasks. In this article, we proposed a multi‐objective decision‐making model for maximizing the system reliability of a multi‐component systems. The objective functions represent the reliability of the sub‐systems arranged in series comprising of components arranged in parallel forms. The cost and total time incurred in the repair/replacement activities are considered as two sets of constraints in the proposed model. Fuzzy logic is incorporated to deal with the inherent uncertainty that exists in the parameters involved in the proposed model. To solve the uncertain multi‐objective selective maintenance model, we have proposed four‐valued neutrosophic technique. The proposed solution methodology combines the concept of four‐valued neutrosophic fuzzy set with fuzzy goal programming technique. Four linear membership functions for the degrees of truth, uncertainty, contradiction, and false are used to obtain the compromise solution through the proposed technique. To determine the feasibility and applicability of the proposed model and solution methodology, a case problem of multi‐component system of an aircraft gas turbine engine is studied.

Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty

Selective maintenance with multiple repair channels has received increasing attention in recent years. The problem aims to jointly identify a subset of units to be maintained and assign the selected maintenance tasks to multiple repair channels. Existing works, however, all assumed that the time durations of maintenance tasks and breaks are deterministic. Due to the randomness of the starting time of future missions and the variation of efficiency of repair channels, these two quantities inevitably possess uncertainty. In this article, by taking account of the uncertainties associated with the time durations of maintenance tasks and breaks, a new selective maintenance model with multiple heterogeneous repair channels is formulated. The new selective maintenance problem decides not only a subset of units to be maintained and the corresponding maintenance tasks, but also the number of repair channels with their specific skill levels and the sequences of maintenance tasks in each repair channel. The objective is to maximize the probability of a system successfully completing the next mission subject to a limited maintenance budget. The resulting optimization problem is resolved via a double-loop algorithm embedded with an ant colony optimization. A five-unit system and a multi-state coal transportation system are presented to demonstrate the effectiveness of the proposed method.

Selective maintenance of multi‐state systems with the repairperson fatigue effect and stochastic break duration

AbstractSystems 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.

A two-stage stochastic programming model for selective maintenance optimization

The problem of selective maintenance exists in many multicomponent systems carrying out an alternating sequence of missions, with scheduled breaks where only a limited number of components can be maintained due to time limit. In this paper, a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions. Traditionally, future operating conditions that affect failure time distribution when calculating reliability in selective maintenance models were assumed as deterministic. In this study, future operating conditions are assumed to be uncertain. The system is subject to several uncertain condition scenarios of exposure, conditional, usage, stress, etc. Each scenario is modeled with its associated occurrence probability. The presented model is a two-stage stochastic mixed-integer nonlinear programming model with fixed recourse, where the first stage is associated with maintenance decisions made before uncertainties are revealed, and the second stage is modeled as a recourse function which is related to the occurrence probability of system failure. A numerical example of a series-parallel system is used to demonstrate the effectiveness of the suggested model.

Selective maintenance on a multi‐state system considering maintenance task assignment and operating cost

AbstractFor 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.

Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations

Systems are often required to execute a sequence of missions separated by scheduled breaks. To improve the success rate of the next mission, multi-mission selective maintenance is applied in the limited break. In general, the durations of missions, maintenance actions, and breaks are stochastic due to the influence of repairpersons, the environment, and so on. However, the existing researches rarely consider the stochasticity of these three kinds of durations in the multi-mission selective maintenance problem, which leads to the underestimation of the grand total cost and the neglect of its stochasticity. Besides, the real system reliability may be lower than the minimum requirement if the stochasticity of durations is ignored in the maintenance decision-making. To overcome the above problems, a new multi-mission selective maintenance and repairpersons assignment model with stochastic durations is developed. The proposed model is transformed into an optimization problem constrained by the limited maintenance resources, and the objective is to minimize the expected grand total cost with a given reliability threshold. Then, a tailored genetic algorithm with the initial population generated by the uniform design is proposed to solve the optimization problem. Finally, numerical examples are carried out to demonstrate the effectiveness of the proposed method.

A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms

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.

Optimal Selective Maintenance Decision-Making for Consecutive-Mission Systems with Variable Durations and Limited Maintenance Time

Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

Selective maintenance of multi-state series systems considering maintenance quality uncertainty and failure effects

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.

Selective Maintenance on a Multi-State Transportation System Considering Maintenance Sequence Arrangement

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.

Selective maintenance model and its solving algorithm for complex system

Selective maintenance model and its solving algorithm for complex system

Selective maintenance modeling and analysis of a complex system with dependent failure modes

In this paper, we propose a statistical and optimization framework for selective maintenance of a complex system with components that have multiple failure modes. Specifically, we study the case where each component has multiple instantaneous hard failure modes and a single soft failure mode. An imperfect maintenance action reduces the effective age and degradation level of a component while increasing the mean degradation parameter and the failure rates of hard failures. A numerical example illustrates the application of the proposed framework and the effectiveness of the Genetic Algorithm and Differential Evolution heuristics in solving such maintenance optimization problems.

Fleet-Level Selective Dispatch and Imperfect Maintenance Strategy Optimization Based on Evolutionary Co-Petition Game Theory

Fleet-level selective dispatch under imperfect maintenance oriented to mission reliability is a practical, multi-level maintenance strategy decision problem that becomes highly difficult since the number of potential maintenance strategies dramatically increases. In this paper, the fleet-level selective imperfect maintenance model is established, and an evolutionary co-petition game theory based approach is proposed for optimizing the fleet maintenance strategy. During the route of the proposed method, each piece of equipment is considered as a game player, and an evolutionary co-petition game strategy is adopted for the dispatch and standby groups in each game round: a cooperative game strategy with strategy space generation way for the dispatch group to decrease the maintenance cost; and a competition game strategy between the dispatch and standby groups to select the optimal dispatch equipment. And a termination condition is given upon which the optimized maintenance strategy is achieved. A case study is further conducted on a fleet of 15 pieces of equipment under 5-level imperfect maintenance actions, and is compared with numerical simulation method. And the results validate that the proposed method possesses high efficiency and effectiveness in optimizing the fleet-level selective imperfect maintenance planning problem.

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

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.