Maintenance Window Research Articles

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.

Railway maintenance windows: Discrepancies between planning and practice in Sweden

Efficient and timely maintenance is essential for the robust operation of infrastructure across all types of transport. In the railway sector, track maintenance presents unique challenges. Specifically, some maintenance tasks require track closures, which can disrupt train schedules. Coordinating these closures with regular train operations makes the scheduling process complicated. To ensure sufficient time for maintenance and prevent conflicts with train operations, the Swedish Transport Administration introduced “maintenance windows.” Maintenance windows, ranging from two to six hours, are periods designated for maintenance, with the track free from train operations. They are set before finalizing the annual train timetable. In this study, we examined the practical application of maintenance windows on a railway line in Southern Sweden from 2019 to 2020. Our findings reveal that while 10% of the total line capacity was allocated for maintenance windows, 11% of the capacity was used for maintenance. Alarmingly, 68% of trackwork occurred outside maintenance windows, and only 34% of the reserved maintenance window capacity was utilized. There is a significant discrepancy between the time and location reserved for maintenance windows and their actual utilization. To address this, we propose aligning maintenance windows more closely with contractor needs by 1) incorporating maintenance windows on the weekends, 2) making maintenance windows available closer to the end of the planning horizon, 3) extending the duration of some maintenance windows, and 4) designing and integrating incentives to encourage contractors to operate within planned maintenance windows.

How should storm surge barrier maintenance strategies be changed in light of sea-level rise? A case study

Sea-level rise and changes in storminess, together with population growth and coastward migration, are increasing the risks of coastal flooding. The impacts are amplified in coastal cities due to the high concentration of inhabitants, infrastructure and services in low lying areas. Many coastal cities are located in estuaries, and storm surge barriers are often constructed to provide flood protection in these areas with long exposed coastlines. For these complex and unique structures, maintenance is vital to ensure they remain reliable and comply with legal protection standards. To ensure safe conditions for workers, storm surge barriers typically define water level thresholds, at which maintenance work must stop when these water levels are reached or exceeded. This paper evaluates the changes in past and future water levels exceeding the maintenance threshold to inform management, maintenance and operation strategies, and design, of storm surge barriers. The Maeslant barrier in Rotterdam, Netherlands is used as a case study to test this analysis. Water levels from measurements taken at the Hoek van Holland tide gauge are compared to the maintenance threshold level. The number of past threshold exceedances is determined and the sensitivity of exceedances to threshold level are assessed. Results show that the maintenance threshold has been exceeded 991 times of which 13% occurred during the maintenance season. Conversely, there were periods in the storm season when water levels were below the maintenance threshold and work could have been carried out safely. The effect of sea-level rise and natural inter-annual tidal cycles on future threshold exceedances is also assessed. Findings reveal that the maintenance window will shift earlier in the year and narrow until exceedances of the maintenance threshold occur regularly all year around. With 1 m sea-level rise maintenance threshold exceedances are likely to occur regularly all year around by 2048. This analysis highlights that maintenance strategies at the Maeslant barrier will need to be adapted for the barrier to remain operational until its design life of 2 100. This is due to the increase in maintenance threshold exceedances resulting from natural interannual tidal cycles combined with sea-level rise. This analysis framework is applicable to existing barriers worldwide to assess future intervention points and for barriers in the design phase to verify the implications of design decisions on planned maintenance.

Feasibility study on the solution of replacing track slab with lateral pushing rail in one maintenance window time

The slab ballastless track is the primary type of high-speed railway. When the track slab in the ballastless track is severely damaged, the typical way to replace it requires cutting the jointless rail above the damaged track slabs. This paper discusses a solution of pushing the rail in the lateral direction (i.e., without cutting the rail) to replace the track slab. To study the feasibility and reliability of this solution, a finite element model for simulating the construction process of rail pushing is established. The necessary spacing of the support slides and the capability of the pushing device are analyzed. The influence of parameters such as the rail release length and the construction temperature on the mechanical state of the rail after being pushed is discussed. In practice, the repair time is limited to one maintenance window (i.e., 4 h). The control of the stress is critical in repairing progress. The corresponding relationship between the deformation curvature of the rail and the stress is studied, and the number of unlocking fasteners is reduced by optimizing the spacing of the rail support slides, which can effectively reduce the fine-adjustment range of the rail after changing the slab. By calculation and design, a “16 + 8 + 16 m” optimization plan is proposed, which can meet the needs of replacing the track slab in the four-hour maintenance window time of high-speed railway. Finally, the feasibility of the finite element model is verified through the comparative analysis of the monitoring data of the lateral pushing of the rail and the simulation analysis results.

Tri-objective generator maintenance scheduling model based on sequential strategy.

A multi-objective modeling approach is required in the context of generator maintenance scheduling (GMS) for power generation systems. Most multi-objective modeling approaches in practice are modeled using a periodic system approach that caters for a fixed maintenance window. This approach is not suitable for different types of generating units and cannot extend the generator lifespan. To address this issue, this study proposes a tri-objective GMS model with three conflicting objectives based on the sequential system approach that accounts for operating hours and start-up times. The GMS model’s objectives are to minimize the total operation cost, maximize system reliability and minimize violation. The main difference between the proposed tri-objective GMS model and other multi-objective GMS models, is that the proposed model uses a sequential strategy based on operating hours and start-up times. In addition, the proposed model has considered the most important criteria in scheduling the generator maintenance, and this reflects the real-life requirements in electrical power systems. A multi-objective graph model is also developed to generate the maintenance units scheduling and used in developing the proposed Pareto ant colony system (PACS) algorithm. A PACS algorithm is proposed to implement the model and obtain solution for GMS. The performance of the proposed model was evaluated using the IEEE RTS 26, 32, and 36-unit systems dataset. The performance metrics used comprise the GMS model objectives. The experimental results showed that the obtained solution from the proposed tri-objective GMS model was a robust solution by considering the different initial operational hours of the units.

Dynamic performance evaluation of ballastless track in high-speed railways under subgrade differential settlement

Ballastless tracks are widely used to provide high rail smoothness in high-speed railways. However, the differential settlement inevitably develops in the subgrade soil, which poses a great threat to the track performance and riding quality via the train-rail dynamic interaction. Since the ballastless tracks are extremely difficult to repair during the maintenance window, it is quite necessary to carry out preventive maintenance to keep the track in good conditions. In order to relate the railway performance with the profiles of subgrade differential settlement, a three-dimensional train-ballastless track-subgrade model was developed incorporating 64 combinations of settlement wavelengths and amplitudes at the roadbed surface. The numerical results were first verified with the measured velocity responses at the concrete base in the Beijing-Tianjin high-speed railway. Then the dynamic responses of the train-track system caused by subgrade differential settlement were analyzed, including the dynamic displacement of train wheels and track structure, wheel-rail interaction forces and car body accelerations. Railway performance was further evaluated as track degradation, lower riding comfort and risk of train safety based on these indicators at different settlement profiles. Results show that increasing settlement amplitudes result in stronger dynamic wheel-rail interaction and vibration of car body. However, critical settlement wavelengths of 10 m and 10–20 m are found to exist where the wheel-rail interaction forces and accelerations of the car body reach their peak values, respectively. It also reveals that the current design limits on the subgrade settlement of high-speed railways are infeasible for infrastructure managers to evaluate the railway status or organize the maintenance works. Moreover, the wheel-rail interaction forces are more credible to determine the railway status than the car body acceleration.

Towards a priority rule to integrate maintenance operations into production schedules

Industry 4.0 techniques provide additional data on the current state of production systems which can be used to adapt and improve the scheduling. In previous work, a data-driven approach for production control of dynamic job shops has been developed, which reacted in real-time on occurring events but handled maintenance only reactive. This approach will be extended towards preventive maintenance by developing an integrated method for scheduling production and maintenance tasks simultaneously. This paper presents an approach of a priority rule which will be used to ensure the execution of maintenance operations but still offers the flexibility of the concrete time of execution in a small maintenance window. The proposed rule is tested in a simulation and shows the benefit against benchmarks.

Integrated Optimization of Maintenance Window and Train Timetable: An Innovation Modeling Approach and Alternating Direction Method of Multipliers Methods

Integrated Optimization of Maintenance Window and Train Timetable: An Innovation Modeling Approach and Alternating Direction Method of Multipliers Methods

Feasibility Study on the Solution of Replacing Track Slab with Lateral Pushing Rail in One Maintenance Window Time

Feasibility Study on the Solution of Replacing Track Slab with Lateral Pushing Rail in One Maintenance Window Time

Generator Maintenance Scheduling using Exchange Market Algorithm

The Generator Maintenance Schedule model is formulated mathematically as a highly constrained combinatorial optimization problem and it is obligatory to implement a suitable optimization tool to determine the best feasible maintenance schedule. The maintenance schedule obtained has to meet a number of power system constraints. There is increased research in the development of approximate solution methodologies such as heuristic and meta-heuristic techniques [1]. Unlike mathematical methods, metaheuristics can obtain an optimal solution to a complex problem fast and are not subjected to limitations such as linearity, continuity, differentiability and convexity that are faced by mathematical programs [2]. This work presents the application of Exchange Market Algorithm (EMA) to find an optimal maintenance schedule. The algorithm is customized to achieve the following:•Selecting the initial population within the maintenance window constraint to enable faster convergence.•Adapt the algorithm to give discrete solutions.•Penalty function included for constraint handling.

Quasi-Periodic Replacement Policy for a Two-Unit Production System

Maintenance optimization of periodic replacement for mono-unit production system is a widely discussed topic. In practice, the replacement for a parallel production system with multi-unit served by one repairman is universal. The replacement action is often implemented in a maintenance window at random, although the replacement period appears as a quasi-periodic event. To optimize these maintenance policies, this study analyzes a bivariate quasi-periodic replacement policy (T, W) for a two-unit parallel production system with a maintenance window and one repairman. One unit of the system is replaced when either a failure occurs or a replacement plan comes in the maintenance window, whichever comes first. If one of the two units is in the replacement, the other may continue operating or waiting until the replacement is completed. An optimal replacement window [T*, T* + W*] can be obtained by jointly considering the system’s long-running cost rate and the availability using the genetic algorithm. This study also introduces three examples of the production system with different types of units to illustrate the proposed policy.

Optimal sequential preventive maintenance policy for a repairable system with maintenance windows

Optimization of sequential preventive maintenance has been widely studied. However, some issues need further discussion when considering certain real factors. Hence, an sequential preventive maintenance policy for a mono-unit repairable system with maintenance windows is proposed. The load strength and catastrophic failures of a system are considered in the reliability model, and an optimization of the SPM policy is rigorously proven by an analytical method. The existence and uniqueness of the optimal solution with a monotone increasing failure intensity function are presented in detail. The proof of theory indicates that the length of the maintenance window has an upper bound. Finally, a numerical example and some sensitivity analyses are given to illustrate the proposed sequential preventive maintenance policy. Results show that waste may occur when performing the general sequential preventive maintenance policy and may increase along with the length of the maintenance windows.

Proactive maintenance scheduling in consideration of imperfect repairs and production wait time

Wait time due to the exhaustion of raw materials or lack of demand is common in manufacturing/production processes, which provides cost-effective maintenance opportunities. This paper designs a two-phase opportunistic maintenance framework based on defect information, which integrates properties of production waits into the decision-making process. At the first phase, a finite number of inspections are executed to reveal the defective state, followed by imperfect preventive repair. At the second phase, no maintenance action is taken until reaching a scheduled maintenance window (postponed maintenance) or arrivals of production waits (opportunistic maintenance). The integration of imperfect repair with postponed and opportunistic maintenance enables a sufficient utilization of remaining useful lifetime and a flexible resource allocation. Under the constraint of the steady-availability requirement, the optimal policy minimizing the system maintenance cost is obtained via the genetic algorithm. A case study from a steel convertor plant shows that our policy outperforms some classic maintenance policies.

Issues of the vertical blanket segment architecture in DEMO: Current progress and resolution strategies

Due to the limited irradiation lifetime of the structural material used for in-vessel components in DEMO, it will be necessary to replace all breeding blankets within the given planned maintenance window in order to meet DEMO availability targets (Romanelli, 2012; Ellis, 2017) [1,2]. It is assumed that failure of in-vessel components cannot be excluded, whilst in-situ repair is unrealistic. Hence the replacement of individual breeder blankets must be technically feasible. As such, remote maintenance replacement of the breeding blankets is a mission critical operation. The baseline concept utilises vertical segment architecture to aid in the removal of the blankets (Iglesias, 2013) [3]. This choice impacts on the tokamak and plant architecture and also affects operational maintenance strategy. Within the EUROfusion PPPT program efforts have been made to perform cross work package investigations on eight Key Integration Issues needed to show the feasibility of the DEMO pre-concept design (Bachmann, 2017) [4]. Key Design Issue 4 is an investigation into the feasibility of the Vertical Segment Architecture blanket feasibility The present work documents the approach, current progress and developments within this investigation. This includes the strategy, identified risks and proposed solutions.

Group maintenance scheduling for two-component systems with failure interaction

• A novel failure interaction framework is constructed considering hazard rate increment. • Environmental uncertainties are incorporated into dependent failure processes. • A new group maintenance strategy is developed to prevent dependent failures. • The applicability is validated by a case study on an electrical distribution system. Failure dependence is a common phenomenon in complex and large-scale industrial plants, which may cause tremendous economic losses and unacceptable safety risks. This paper investigates an advanced group and opportunistic maintenance policy for a two-component system with failure interaction. The interaction between soft and hard failures is characterized by random hazard rate increments, with fully consideration of environmental uncertainties. To sufficiently utilize the economic dependence between components, Scheduled maintenance windows are equally spaced for group maintenance of the whole system. Whenever a component is replaced at a maintenance window, the other one is also opportunistically replaced. Furthermore, failure of component 2 is removed by minimal repair, whereas replacement is left to the subsequent window. The objective is to jointly optimize the maintenance interval and the number of maintenance windows via the artificial bee colony algorithm. A case study on an electrical distribution system is provided to validate the applicability of the adopted policy.

Prediction model with optimal matching parameters for a dynamic track stabiliser during railway maintenance

Nowadays, high-speed and heavy duty trains make ballasted track extremely busy, and thus it is necessary to solve the conflict between the traffic density and the maintenance work load. However, since the mechanical properties of discrete ballast bed are complex, there is a lack of in-depth investigation into the working performance of large-scale railroad maintenance machinery. In this paper, we take the WD-320 dynamic track stabiliser as the research object, to study the effect of operation parameters on the quality state of the ballast bed. Based on the field test data, a prediction model for optimal matching of operation parameters has been constructed, which can be used to estimate, compare and determine the optimal operation parameter combination for the operation process. By operating according to the optimal operation parameter combination, the optimum quality state of the ballast bed can be quickly reached, to solve the conflict between the traffic density and the necessary maintenance window.

Prediction model with optimal matching parameters for a dynamic track stabiliser during railway maintenance

Nowadays, high-speed and heavy duty trains make ballasted track extremely busy, and thus it is necessary to solve the conflict between the traffic density and the maintenance work load. However, since the mechanical properties of discrete ballast bed are complex, there is a lack of in-depth investigation into the working performance of large-scale railroad maintenance machinery. In this paper, we take the WD-320 dynamic track stabiliser as the research object, to study the effect of operation parameters on the quality state of the ballast bed. Based on the field test data, a prediction model for optimal matching of operation parameters has been constructed, which can be used to estimate, compare and determine the optimal operation parameter combination for the operation process. By operating according to the optimal operation parameter combination, the optimum quality state of the ballast bed can be quickly reached, to solve the conflict between the traffic density and the necessary maintenance window.

Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.

A Branch-and-Bound Algorithm for Minimizing the Total Tardiness of a Three-Agent Scheduling Problem Considering the Overlap Effect and Environmental Protection

Due to serious air pollution, some machines may be forced to shut down for short periods. During such periods, companies will make no progress on the yields. Maintenance also leads to reductions. If we view such two indispensable things such as shutdown and maintenance as jobs, the two can overlap with each other, i.e., the schedule can be shorter. However, this phenomenon is seldom discussed in scheduling. In this paper, we consider a three-agent scheduling problem with the overlap effect. The objective is to minimize the total tardiness of jobs from agent 3 under the restrictions that the PM 2.5 emissions of agent 1 cannot exceed a limit and that each maintenance activity requested by agent 2 must be conducted within its specific maintenance window. A branch-and-bound algorithm is developed to generate the optimal schedules. Computational experiments are conducted to show the solution quality and execution speed of the proposed algorithm.

A two-phase preventive maintenance policy considering imperfect repair and postponed replacement

This paper investigates a novel two-phase preventive maintenance policy for a single-component system with an objective of maximizing the revenue generated by the performance-based contracting (PBC). The system undergoes a defective state before failure, and produces signal hinting the condition. The maintenance policy consists of two phases: imperfect maintenance phase followed by postponed replacement phase. In the imperfect maintenance phase, inspection is performed to reveal the defective state, leading to a possible repair. Both the inspection and the repair are imperfect. In the postponed replacement phase, preventive replacement is performed during the upcoming scheduled maintenance window, before which no inspection or repair is executed. The expected net revenue under PBC is maximized via the joint optimization of the inspection interval, number of inspection and preventive replacement interval. We apply the model to a case from a steel converter plant, and the results show that our proposed maintenance policy outperforms some existing maintenance policies in terms of the net revenue.