Opportunistic Maintenance Policy Research Articles

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.

Optimal condition-based opportunistic maintenance policy for two-component systems considering common cause failure

This paper introduces a maintenance optimization model designed for a system comprising two distinct components that are arranged in series and susceptible to common cause failure. Both the components undergo dependent degradation and shock processes whose performance levels are periodically inspected. A condition-based opportunistic maintenance policy is considered, indicating that either preventive or corrective maintenance may be conducted depending on the deterioration state observed during inspections. Meanwhile, when maintenance is performed on one component, the other can be opportunistically maintained. An optimization problem is then formulated under the framework of semi-Markov decision processes, in order to find the optimal preventive and opportunistic maintenance thresholds for the two components. Three algorithms are introduced to address this problem, namely the improved genetic algorithm, the cyclic coordinate search algorithm, and the enumeration algorithm. Their effectiveness is demonstrated through a case study focusing on offshore wind turbines. The result shows that the efficiency of the improved genetic algorithm is significantly superior to the other two algorithms.

Maintenance modeling of serial-parallel multi-station manufacturing system with failure-induced damage and assembly parts

This paper studies the joint optimization of maintenance scheduling and spare part management for Serial-Parallel Multi-station Manufacturing Systems (SPMMS) with failure-induced damage, which implies the failure of one component may induce the damage of other structurally-connected components. In the proposed hybrid spare part usage policy, either the assembly part, which is a pre-assembled spare part module, or the combination of multiple unassembled single parts can be used to retore the failed station. On the basis of the introduced reliability model, the hybrid usage policy is combined into the joint analysis of spare part inventory and corrective maintenance actions. Then, an opportunistic maintenance policy with the greedy algorithm is developed to reduce the computing complexity. The numerical examples show that the cost advantage of assembly parts and the hierarchical inventory sharing of spare parts can jointly affect the cost effectiveness of the SPMMS. The proposed hybrid spare part usage policy is more cost-effective than the ordinary usage policy, the substitution policy and the component-level policy. The policy comparisons among different hierarchical sharing policies also show that both the division criterion of failure states and the usage priority of spare parts have significantly impacts on the cost effectiveness of the SPMMS.

Joint optimization of maintenance and spares inventory policy for a series-parallel system considering dependent failure processes

This paper investigates the joint optimization of condition-based opportunistic maintenance and spare inventory policies for a series-parallel system consisting of software and components. Software failure follows a homogeneous Poisson process. Components are subject to mutually dependent competing failure processes. When the software failure occurs, an opportunistic inspection will be implemented for the components. Besides the maintenance policy, the (s, S) inventory policy is applied for components. The maintenance and order decisions will be made according to the conditions of the components and inventory level at each inspection. The state transition probabilities and expected sojourn times can be obtained by the formulated semi-Markov decision process. The optimal inspection interval, the preventive replacement threshold of the component, and the order point can be derived by minimizing the expected average cost rate. Finally, the effectiveness and superiority of the proposed joint optimization model are verified by an illustrative example.

Degradation modeling and opportunistic maintenance for two-component systems with an intermittent operation component

In this study, an opportunistic predictive maintenance framework was developed for a system comprising one continuous operation component (COC) and intermittent operation component (IOC). The COC operates consistently with the system. In contrast, the IOC operates alternately in operation and standby modes. The degradation of the COC can increase the working frequency of the IOC, which can accelerate its degradation process. Omitting this degradation interaction in degradation modeling and reliability assessment could result in inaccurate lifetime prognostic and suboptimal maintenance plans. Therefore, in this study, a degradation model for the IOC is proposed considering the influence of the degradation state of the COC. A reliability model for the IOC was derived and used for predictive maintenance. Based on the proposed degradation models, an opportunistic predictive maintenance policy was proposed, considering the structural and economic dependencies between the components. Finally, a case study of an air compressor system is conducted to demonstrate the viability and benefits of the proposed model.

Hybrid opportunistic maintenance policy for serial-parallel multi-station manufacturing systems with spare part overlap

Hybrid opportunistic maintenance policy for serial-parallel multi-station manufacturing systems with spare part overlap

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.

Hierarchical-clustering-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems

Both the spare part provision and the maintenance scheduling are important in ensuring the production of the Serial-Parallel Multi-Station System (SPMMS). In the multi-specification and small-batch production, the production plan, which renews batch by batch, leads to variable and uncertain working conditions. The existing Dynamic Opportunistic Maintenance (DOM), which separately optimizes each cycle, ignores the aftereffects of both the spare part provision and the Preventive Maintenance (PM) on the following PM scheduling. This paper studies the dynamic joint optimization of spare part provision and maintenance scheduling for the SPMMS, where the aftereffects of the information are especially investigated. During the failure rate modelling, the aftereffect of the historical working conditions is described with the accelerated failure-time model. In the system-level maintenance modelling, a Hierarchical-Clustering-Based Opportunistic Maintenance (HCBOM) policy, which uses a non-parametrical clustering, is introduced to incorporate the aftereffects of the PM. The case study and the policy comparisons verify the above-mentioned aftereffects. The proposed HCBOM is more cost-effective than the tradition Opportunistic Maintenance (OM) frame, the DOM, the group maintenance and the station-level maintenance. From the perspective of clustering, this work provides an innovative OM policy, which can guide practitioners to formulate cost-effective PM scheme without time-expensive calculations.

Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement

Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement

Predictive Maintenance and Upgrade Scheduling for Production Line under Multi-period Product-service Paradigm

In the rapidly developing product–service paradigm, a multi-unit manufacturing system will be leased many times during life cycle. Besides maintenance programming for a single leasing period, an upgrade policy is essential to achieve life-cycle profit optimization during sequential periods. This article presents a framework integrating opportunistic maintenance scheduling and upgrade decision for the leased system throughout its life cycle based on a real-time prognostic method. With machine-level prognosis updating, system-level opportunistic predictive maintenance is dynamically achieved by leasing profit-saving calculations during each leasing period. Between successive leasing contracts, the optimal upgrade levels are dynamically determined through upgrade profit-saving maximization. A numerical example of a leased manufacturing line demonstrates the long-term economic effectiveness of this policy. The proposed framework extends traditional opportunistic maintenance policies in both the machine-level prognosis and the system-level upgrade decision. This framework can be applied to a broad range of leased systems with longer service lifetimes.

Condition-based opportunistic maintenance policies with two-phase inspections for continuous-state systems

This paper proposes two condition-based opportunistic maintenance policies with two-phase inspections for continuously degraded systems with real-time monitoring and periodic inspections, respectively. The degradation of the components and the system is modeled by a Gamma process. Existing literature finds the optimal maintenance strategies by monitoring the performance of all components in a multi-component system. However, it maybe unrealistic and costly to perform inspections on all components for certain systems. Hence, this study considers a two-phase inspection method, that is, the system-level inspection is conducted first, and then the decision for component-level inspection is made based on the system performance. Maintenance is performed to the component when its degradation reaches the preventive maintenance (PM) threshold or the corrective maintenance (CM) threshold at a component-level inspection. Whenever a maintenance action is implemented, the other components whose degradation exceeds an opportunistic maintenance (OM) threshold will be repaired. Then the long-run expected cost for two-component systems and multi-component systems are derived based on the semi-regenerative properties and simulation method. The combination of the system-level inspection interval, OM, PM and CM thresholds is determined to minimize the long-run expected cost. Finally, this study applies the proposed maintenance policies to the battery pack systems in electrical vehicles.

Technician Collaboration and Routing Optimization in Global Maintenance Scheduling for Multi-Center Service Networks

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

Hypergraph-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems

This paper studies the joint modeling of spare part provision and maintenance scheduling for Serial-Parallel Multi-station Manufacturing Systems (SPMMS), where the spare part connections are especially investigated. In the SPMMS, multiple types of spare parts are retained for uncertain failures of the stations, and multiple stations may share the spare parts in inventory. To describe the spare part connections among the stations, a hypergraph-based model is introduced. In the system-level maintenance, the SPMMS is formulated to be a multi-connection hypergraph, with the Spare Part Hyperedges (SPH), the cost-saving hyperedges and the capacity-loss hyperedge involved. Based on the multi-connection hypergraph, a Combination-Spread Opportunistic Maintenance (CSOM) policy is proposed. To obtain the optimal alternatives of the maximum inventory levels, the length of the combination window and the length of the spread window, a hierarchical optimization method is developed. The case study and the comparisons show that the proposed hypergraph-based CSOM policy is more cost-effective than the traditional maintenance policy, the station-level maintenance policy and the fixed-group maintenance policy. Finally, the discussion shows that the traditional maintenance model is a special scenario of the hypergraph-based model.

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.

Opportunistic perfect preventive maintenance policy in presence of masked data

In this paper, the maintenance optimization problem of multi-component system is considered. It is assumed that the exact cause of system failure might be masked. That is, the exact cause of failure is unknown, and we only know that it belongs to a set called mask set. Both opportunistic perfect preventive maintenance (OPPM) and perfect corrective maintenance are considered. Threshold of OPPM and inter-inspection interval are considered as decision parameters which are optimized using long-run cost rate criteria. The applicability of the proposed maintenance policy is investigated using an illustrative example.

A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies

Condition-based maintenance (CBM) optimization utilizing prognostic information and methods for complex systems has attracted increasing attention. This paper presents a bi-level approach to optimize a CBM policy for multi-component systems subject to stochastic and economic dependencies. The objective of the decision rule at the system level is to address if preventive and/or corrective maintenance actions are necessary regarding the predictive reliability of the system. At the component level, the aim is to identify the optimal group of components to be preventively maintained when maintenance is triggered due to the decision made at the system level. A maintenance efficacy indicator is introduced to prioritize components to be maintained by taking into account both the resulting reliability improvement quantity and the maintenance cost incurred. The optimal inter-inspection interval, the thresholds of system and component conditional reliability for triggering maintenance are jointly determined for minimization of the long-run average cost rate. A numerical study and sensitivity analysis on a 2oo3:G system are carried out to validate the performance of the proposed maintenance policy. Compared to another opportunistic maintenance policy without considering the system health condition, the proposed CBM policy leads to more cost gain and effectiveness in the maintenance decision-making.

Energy Consumption-Based Maintenance Policy Optimization

The optimal predictive, preventive, corrective and opportunistic maintenance policies play an important role in the success of sustainable maintenance operations. This study discusses a new energy efficiency-related maintenance policy optimization method, which is based on failure data and status information from both the physical system and the digital twin-based discrete event simulation. The study presents the functional model, the mathematical model and the solution algorithm. The maintenance optimization method proposed in this paper is made up of four main phases: computation of energy consumption based on the levelized cost of energy, computation of GHG emission, computation of value determination equations and application of the Howard’s policy iteration techniques. The approach was tested with a scenario analysis, where different electricity generation sources were taken into consideration. The computational results validated the optimization method and show that optimized maintenance policies can lead to an average of 38% cost reduction regarding energy consumption related costs. Practical implications of the proposed model and method regard the possibility of finding optimal maintenance policies that can affect the energy consumption and emissions from the operation and maintenance of manufacturing systems.

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.

An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models

This paper proposes an opportunistic maintenance policy for maintaining mechanical systems consisting of two series units with economic dependency. Condition monitoring and age information is available for unit 1 and unit 2, respectively. A gamma process describes the stochastic deterioration of unit 1, and condition monitoring data is utilized to estimate its hazard rate using the proportional hazards model (PHM). A matrix-based algorithm is employed to derive the conditional reliability of a two-unit series system with a continuous-state deterioration process. The policy proposes preventive actions on scheduled intervals, based on lifetime and PHM parameters, to enhance system reliability and mitigate unexpected downtimes. Corrective actions rectify the failed unit, while the other unit can be maintained opportunistically. The problem is formulated as a semi-Markov decision process (SMDP) with an objective of minimizing the long-run expected average cost over an infinite time horizon. This paper relaxes a common assumption that repair brings the units to the as-new condition, and provides solutions for the maintenance scheduling of two-unit series systems, where repair is also allowed.