Opportunistic Maintenance Model Research Articles

Grouping-maintenance of multi-components production system under ecological background

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle.Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions.Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs.Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems.Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Efficient opportunistic maintenance strategies via pruning in parallel–series systems with economic dependence

Opportunistic maintenance stands out as an effective strategy for minimizing maintenance costs and enhancing system efficiency. Despite its importance, current opportunistic maintenance models are often tailored to specific system types, particularly those with series structures, or they offer heuristic approaches for multi-component redundant systems. This paper shifts focus to the most common of such systems, the parallel–series system. We approach the optimization of opportunistic maintenance in a parallel–series system through a Markov Decision Process framework, utilizing phase-type approximations of individual component hazard functions. Addressing the challenge of the curse of dimensionality in the action space, this study conducts a thorough structural analysis of the optimal opportunistic maintenance policy, deriving a pruning procedure, which can reduce the extensive combinatorial action space into a more manageable linear complexity without sacrificing the strategy’s effectiveness in parallel–series systems. Building on this more manageable action space, we introduce a more efficient algorithm leveraging the proposed pruning procedure for determining the optimal opportunistic maintenance policy. The effectiveness and practical applicability of this approach are rigorously demonstrated through comprehensive numerical experiments.

Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes

This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.

Adaptive maintenance window-based opportunistic maintenance optimization considering operational reliability and cost

Opportunistic maintenance (OM) reduces maintenance costs by combining the maintenance of multiple components. It has recently been widely used in complex systems. However, few studies have considered that advance maintenance results in the insufficient utilization of reliability. Besides, the applicable conditions for advance and postpone maintenance are ignored in existing literature. In this paper, the economic benefits and failure risk of advance and postpone maintenance are evaluated to fully utilize component's reliability. In addition, all maintenance types (preventive maintenance, corrective maintenance, and replacement) and programs (advance and postpone maintenance) for critical components and non-critical components are discussed based on the degree of overlap of the components’ maintenance windows. Then, an operational reliability (OR) model based on system availability is established to reflect the operating state of the system under damage and maintenance. In order to maximize OR and minimize maintenance costs, a multi-objective optimization model for an OM strategy based on adaptive maintenance window is proposed. Finally, a case study of a propeller aircraft system is conducted to verify the proposed model. It proves superior to the traditional OM and other models in terms of cost and OR.

Opportunistic maintenance modeling for series production systems based on bottleneck by considering energy consumption and market demand

ABSTRACT Nowadays, series production systems are broadly used in various branches of industry. In these kinds of production systems, any disruption in production flow can significantly increase production cost and time; therefore, a proper maintenance strategy considerably reduces the company’s costs and enhance organizational performance. In this research, a novel bottleneck-centered opportunistic maintenance policy for a series production line is proposed. First of all, the bottleneck is detected by the Lowest Production Rate method. Then, preventive maintenance time interval, reliability threshold, and number of maintenance cycles are determined by the particle swarm optimization algorithm. Finally, a novel opportunistic maintenance strategy is proposed based on analyzing the cost savings of preventive maintenance advancement and postponement. To demonstrate the model’s effectiveness, the proposed bottleneck-based opportunistic maintenance model is compared with two other maintenance models, and the results confirmed that the proposed strategy could efficiently eliminate unnecessary production breaks and achieve cost reduction.

Optimal rearrangement and preventive maintenance policies for heterogeneous balanced systems with three failure modes

This paper studies a heterogeneous balanced system composed of multiple interchangeable components. The degradation process of components is described by a gamma process, and deterioration rates in different positions are different due to the effect of loading stress or temperature. Three competing failures may occur: a) shock failure caused by environment shocks, b) soft failure when the deterioration level of any component exceeds a critical value, and c) out of balance when the difference value among components reaches the failure threshold. To avoid system failure, a rearrangement action is adopted to change the position of components. Besides, preventive maintenance is considered when a component deteriorates severely. A semi-Markov decision process (SMDP) is developed to obtain the optimal policy by minimizing the average maintenance cost. To facilitate calculation, the data transmission method is used to convert the semi-Markov decision model to the Markov decision model, and a value iteration algorithm is established to obtain the optimal maintenance action at each state. Considering practical implications, an imperfect preventive and opportunistic maintenance model is formulated under the SMDP framework. Finally, a typical tire rotation problem in motorcycles proves that the imperfect maintenance policy outperforms the other policies when replacement fees are more expensive.

Multi-component system maintenance optimization of a rail transit train based on opportunistic correlations

Abstract In order to deal with the problems of insufficient or excessive maintenance in the current maintenance activities of China transit trains, this paper develops a novel multi-component system maintenance optimization approach based on an opportunistic correlation model. Based on the minimal reliability and failure rate change rule of each train component, the novel proposed maintenance optimization benefits from an improved opportunistic maintenance model with system structure correlation, fault correlation and reliability correlation under imperfect maintenance. Then, different maintenance modes can be determined by a proposed maintenance factor under the different conditions of components. Specifically, the reliability threshold of each component is also considered to optimize the maintenance cost by the system reliability and operational availability of the train. Furthermore, as the mentioned problem belongs to the NP-Hard optimization problems, a modified particle swarm optimization (PSO) with the improvement of inertia weight is proposed to cope with the optimization problem. Based on a specific case under the practical recorded failure data, the analysis shows that the proposed model and approach can effectively cut the maintenance cost.

Distribution network regional opportunity maintenance model design considering total supply capability upgrade of distributed power

Abstract Because the existing maintenance models are not statistically analyzed, the maintenance effect is not ideal. Therefore, a distribution network area opportunistic maintenance model considering the total supply capability promotion of distributed generation is designed to achieve the total supply capability promotion and reduce the network loss rate after maintenance. Considering multiple time scales, the distribution network total supply capability model is constructed, and the improved repetitive power flow algorithm is used to count the model; taking the total supply capability model as the reliability basis of the distribution network system, and the maximum power supply capacity as the goal, the regional opportunistic maintenance model of total supply capability promotion is constructed, and the model is used to determine the area to be repaired, fully considering the reliability requirements of the distribution system, and combining with the node depth coding technology and the simulation Genetic algorithm optimizes the network structure, realizes the network reconfiguration, and completes the model solution. Through the analysis of the actual data, using this model to repair the regional equipment of distribution network can obtain good maintenance results. After the completion of maintenance, the network loss rate is low, but the total supply capability is high, which has good maintenance effect.

Opportunistic maintenance model for load sharing k-out-of-n systems with perfect PM and minimal repairs

This paper proposes a novel opportunistic maintenance model for load-sharing k-out-of-n systems where partial failure is allowed with corrective and preventive maintenance combined. We consider a load-sharing k-out-of-n system with identical components having exponential distribution subject to a constant load. Upon components failure, the workload will be redistributed to the survived components increasing the failure rates. This creates a failure dependency among components which complicates finding the optimal cost-effective maintenance policy. The proposed maintenance model consists of two intervals where minimal repairs are sufficient for the failed components until time In the second interval, two scenarios may occur: Corrective Maintenance (CM) of the failed components together with opportunistic Preventive Maintenance (PM) of the survived ones are performed once failures occur, or we reach the end of policy whichever takes place sooner. Tampered Failure Rate (TFR) model is used to explain the failure dependency and failure rate change of load-sharing components. In this paper, we find the optimal and for the opportunistic maintenance policy that minimizes the total expected cost over the lifecycle of a load sharing k-out-of-n system. We derive the analytical solution and present an application to a computer numerical control (CNC) system of a 3-out-of-7 system to illustrate the proposed model.

Integrated Structural Dependence and Stochastic Dependence for Opportunistic Maintenance of Wind Turbines by Considering Carbon Emissions

Wind turbines have a wide range of applications as the main equipment for wind-power generation because of the rapid development of technology. It is very important to select a reasonable maintenance strategy to reduce the operation and maintenance costs of wind turbines. Traditional maintenance does not consider the environmental benefits. Thus, for the maintenance problems of wind turbines, an opportunistic maintenance strategy that considers structural correlations, random correlations, and carbon emissions is proposed. First, a Weibull distribution is used to describe the deterioration trend of wind turbine subsystems. The failure rates and reliability of wind turbines are described by the random correlations among all subsystems. Meanwhile, two improvement factors are introduced into the failure rate and carbon emission model to describe imperfect maintenance, including the working-age fallback factor and the failure rate increasing factor. Then, the total expected maintenance cost can be described as the objective function for the proposed opportunistic maintenance model, including the maintenance preparation cost, maintenance adjustment cost, shutdown loss cost, and operation cost. The maintenance preparation cost is related to the economic correlation, and the maintenance adjustment cost is described by using the maintenance probabilities under different maintenance activities. The shutdown loss cost is obtained by considering the structural correlation, and the operation cost is related to the energy consumption of wind turbines. Finally, a case study is provided to analyze the performance of the proposed model. The obtained optimal opportunistic maintenance duration can be used to interpret the structural correlation coefficient, random correlation coefficient, and sensitivity of carbon emissions. Compared with preventive maintenance, the proposed model provides better performance for the maintenance problems of wind turbines and can obtain relatively good solutions in a short computation time.

An imperfect age-based and condition-based opportunistic maintenance model for a two-unit series system

Maintenance strategies have been studied extensively and applied successfully to reduce the operation costs of industrial assets. However, the study for heterogeneous two-unit systems is rarely based on the usage and condition information of units, simultaneously. This paper originally proposes a novel imperfect opportunistic maintenance model for a two-unit series system considering random repair time and two types of failures, where unit 1 and unit 2 are respectively subject to soft failure and hard failure. The system maintenance actions are performed not only based on the deterioration level of unit 1 but also on the usage of unit 2. To utilize the economic dependence between the two units, the opportunistic maintenance policy is adopted in the series system. When a unit fails or is being repaired, the other unit has an opportunity to be simultaneously maintained once it meets the requirement of opportunistic maintenance. Herein, opportunistic maintenance action is imperfect maintenance, which means that the deterioration level of unit 1 and the usage of unit 2 restore to a lower level than the as-good-as new status after repair. Nonetheless, once both units meet the requirement of replacement actions, they will be perfectly replaced by new ones. The objective is to minimize the system average maintenance cost by optimizing the thresholds of opportunistic maintenance and perfect replacement of two units. Finally, an illustrative example is presented to demonstrate the superiority of the proposed maintenance model.

Opportunistic maintenance for offshore wind farms with multiple-component age-based preventive dispatch

Operation & maintenance (O&M) costs account for a large portion of total life cycle cost for onshore wind energy, and the amount is estimated to be more for offshore wind energy. Developing a sound opportunistic maintenance strategy is a solution to reduce O&M costs and enhance wind energy's competitiveness. When the wind farm is located offshore, turbines are not only subject to degradation but also the impact from the harsh marine environment. However, the degradation is mainly regarded as the only cause of the failure in the existing opportunistic maintenance models for the offshore wind energy sector. At the same time, too frequent preventive dispatch of maintenance teams exists on some occasions. This paper proposes a maintenance strategy for offshore wind farms integrating three types of maintenance opportunities. In addition to the maintenance opportunities created by degradation failures and incidents, an age-based opportunity is introduced to improve the trigger of preventive dispatch. A numerical example is presented to illustrate the effectiveness of the proposed strategy. The comparative analysis shows 2.6% and 1.5% annual cost can be reduced respectively when compared with two traditional opportunistic maintenance strategies in the base scenario.

Dynamic Opportunistic Maintenance Model of Multi-Component System Considering Degradation Interaction

Dynamic Opportunistic Maintenance Model of Multi-Component System Considering Degradation Interaction

An opportunistic maintenance strategy for offshore wind turbine system considering optimal maintenance intervals of subsystems

Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.

Dynamic opportunistic maintenance planning for multi-component redundant systems with various types of opportunities

Opportunistic maintenance (OM) is a key solution to reduce the maintenance costs or/and to improve the system dependability/performance. However, the existing OM models are mainly developed to specific classes of systems (series structures) with specific types of maintenance opportunities (MOs) such as component failure. The objective of this paper is to develop a dynamic OM approach for multi-component redundant systems such as parallel, parallel-series, series-parallel, and k-out-of-n, etc. Various types of MOs are also considered. To this purpose, a generalized MO model is firstly proposed for the modeling and formulation of existing MOs and new ones. A dynamic OM model with flexible decision rules allowing to consider various types of MOs in maintenance decision-making is then introduced. In addition, a multi-mode logistic support model is also proposed to better incorporate different types of MOs with associated logistic support requirements. To find the best OM scenario, an efficient optimization algorithm using Genetic algorithm with memory is developed. The proposed optimization algorithm allows also updating online an OM maintenance plan in presence of new MOs which may occur with time. The uses and advantages of the proposed OM approach are illustrated through a six-component redundant system.

An opportunistic group maintenance model for the multi-unit series system employing Jaya algorithm

Opportunistic maintenance approaches deal with performing group preventive maintenance (PM) on the other units in a series system due to the intervention of any scheduled PM of a component. Simultaneous maintenance actions show better economic performance due to the direct reduction of downtime costs and production losses. However, it is uneconomical to perform maintenance on all units simultaneously. To address this issue, various simulation and optimization approaches including Markov chains, genetic algorithm etc. have been applied in order to achieve optimum solutions in group maintenance models. However, most of these strategies suffer from intractability as the problem size increases. In the present paper, we develop an efficient opportunistic grouping methodology for the multi-unit series system while considering imperfect preventive maintenance. The aim is to obtain an optimum PM interval and grouping of units to minimize the expected total system maintenance cost per unit time during the mission. A recently developed meta-heuristic named ‘Jaya algorithm’ is applied to optimize the objective function. The effectiveness of the proposed approach is examined with three maintenance models: single unit model, mono-group model and the proposed opportunistic group model. Results reveal that the proposed group maintenance model results in 19% cost savings as compared to the mono-group model and 71% compared to the single component maintenance model.

An improved opportunity maintenance model of complex system

To solve the problems of multiple parts, high cost of disassembly, and imperfect maintenance in multi-component complex systems, an opportunistic maintenance model of multi-component complex systems based on a time threshold is proposed in this article. The influence of the disassembly sequence and hybrid evolution factors on maintenance and learning-forgetting effects are considered in the proposed model. In this article, the learning-forgetting effect of the field strip in the components on the same level or components on different levels is considered in addition to the shortest disassembly path of multi-component complex systems. Imperfect and preventive maintenance is applied in a complex system, and random failure involves minor repairs. Multiple objectives that minimize maintenance costs and maximize operation time rate are proposed. Monte Carlo simulation is used to solve the model and obtain the time threshold of optimal opportunistic maintenance. The results show the feasibility of the opportunistic maintenance model.

A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost.Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy.Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs.Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).

Opportunistic Maintenance Model for Wind Turbine Based on Reliability Constraint

Preventive maintenance of wind turbine can effectively improve the reliability of wind turbine. This paper firstly set single component reliability of wind turbine as constraint condition, single component minimum unit time maintenance cost as objective function to obtain optimal maintenance cycle and maintenance times. On this basis, repairing component which meet certain conditions, then build opportunistic maintenance model of wind turbine. Lastly, set the minimum total cost under opportunistic maintenance as objective function, the wind turbine availability as constraint condition, then solve this model to obtain optimal threshold of opportunistic maintenance and minimum total maintenance cost using genetic algorithm. The example analysis shows that the model can effectively save the total maintenance cost. This study has some reference significance for the maintenance department to make the maintenance plan.

A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms

Operations and maintenance costs of the wind power generation systems can be reduced through the implementation of opportunistic maintenance policies at suitable indenture and maintenance levels. These maintenance policies take advantage of the economic dependence among the wind turbines and their systems, performing preventive maintenance tasks in running systems when some other maintenance tasks have to be undertaken in the wind farm. The existing opportunistic maintenance models for the wind energy sector follow a static decision making process, regardless of the operational and environmental context. At the same time, on some occasions policies do not refer to practical indenture and maintenance levels. In this paper, a maintenance policy based on variable reliability thresholds is presented. This dynamic nature of the reliability thresholds, which vary according to the weather conditions, provides flexibility to the decision making process. Within the presented model, multi-level maintenance, capacity constraints and multiple failure modes per system have been considered. A comparative study, based on real operation, maintenance and weather data, demonstrates that the dynamic opportunistic maintenance policy significantly outperforms traditional corrective and static opportunistic maintenance strategies, both in terms of the overall wind farm energy production and the Life Cycle Cost.