Repair Duration Research Articles

Unreliable service system with strategic customers: effect of abandonments and repair time

AbstractIn this study, we analyze the reneging behavior of strategic customers in a Markovian queueing system with a single server which is subject to breakdowns. In the event of a breakdown, the server is replaced with a secondary backup unit which serves at a diminished service speed. The restoration of the primary unit is possible only when the system is empty and through a repair process. Reneging is possible and the customers may decide to abandon the system at any time. To focus on the effect of reneging, we also consider the case where reneging is prohibited and the customers decide exclusively, whether to join or balk. We also consider the case where the repair duration is instantaneous. For these models, we derive the equilibrium strategies for the customers and evaluate the performance of the system under both scenarios. Moreover, we discuss the effects of the server repairing procedure and present numerical experiments that reveal key differences in the customer strategic behavior. We identify cases where reneging is beneficial in terms of social welfare, as well as cases where prohibiting reneging ensures greater welfare.

Evaluating vehicle repair duration in relation to driving styles

One of the important factors affecting the technical condition of vehicles is driving style. This paper investigates the extent to which vehicle repair duration depends on different driving styles. Driving style was assessed based on average fuel consumption, which is an indicator of driving behavior. Three distinct driving styles were identified: mild, moderate, and aggressive, each characterized by different levels of average fuel consumption. The analyses were carried out on the basis of actual operating data of vehicles included in the fleet of one of the transport companies operating in the city of Lublin, Poland. The results of the study showed how vehicle repair time can be reduced by changing driving style from aggressive to mild, and can help answer the question of whether it is justified to increase drivers' competence in economical driving in order to improve vehicle reliability.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

Assessment of technical and economic indicators of restoration repair of multichannel receiver-transmitter modules of active phased antenna arrays

The article describes the content of unscheduled restoration repair of a batch of multichannel transceiver modules operated as part of the AFAR of a mobile radar complex and exposed to damage. The classification of MPM by the nature of damage is presented and an analysis of the possibility of restoration repair is carried out. A repair route has been developed. Using a previously developed simulation model of the technological process, recovery repair indicators are determined: recovery repair duration for a given batch of products and labor intensity per unit of product. The optimal recovery repair strategy has been chosen for the conditions of a particular production.

Recovery Scheduling of Road Networks Considering Day-to-Day Flow Evolution

Natural disasters can lead to substantial disruptions in road networks, making many critical links unusable. It is important to timely repair the damaged links as they allow transportation of emergency services, relief materials, and so forth, after disasters. Many existing studies that focus on optimal recovery of damaged links after disasters assume that only a single agency is available for repair. Moreover, most of the existing studies do not consider travel time on links to be a function of traffic flow passing through the links and assume that the traffic flow gets distributed based on user equilibrium each time a link is repaired. However, such a traffic distribution is unrealistic as it assumes that the traffic flow remains the same across all the days for which a link is repaired and the traffic distribution gets suddenly modified whenever a link is fully repaired. The goal of this paper is to address these gaps in the literature of disaster recovery. We study the problem of determining the optimal repair scheduling of damaged links to minimize the sum of the total system travel time over the repair duration given that multiple repair agencies are available for recovery. Also, we consider a day-to-day traffic flow evolution where the route choices of travelers depend on the travel conditions of the previous day. We formulate this problem as a mixed-integer non-linear program. We proposed two solution methodologies to solve the problem: a genetic algorithm and a greedy algorithm. We tested these methodologies under different settings.

Distributionally-robust chance-constrained optimization of selective maintenance under uncertain repair duration

The joint selective maintenance and repairperson assignment problem with uncertain duration of maintenance actions is studied in this paper. This problem arises in mission-oriented assets that perform sequences of missions interspersed with scheduled pauses to allow maintenance activities. The goal is to find the optimal subset of components to maintain and the maintenance level to be carried out by each repairperson that maximizes system reliability for the following mission. We first reformulate the complex objective function via single-variable concave functions before applying piecewise-linear approximation. This tight approximation enables large instances of the problem to be solved efficiently. We also consider the commonly encountered case of probabilistic maintenance duration with their distributions estimated from limited historical data samples. Distributionally-robust chance constraints (DRCCs) are employed to ensure that the maintenance tasks assigned to each repairperson can be completed within the break for a set of distributions contained in a Wasserstein-1 ball around the empirical distribution of the field data. A CVaR-based tractable approximation is applied to the DRCCs such that the problem can be reformulated into a mixed integer linear program readily handled by standard solvers. Results from the numerical experiments conducted on large benchmark instances demonstrated the computational efficiency of the proposed models and the added value of considering distributional ambiguity when dealing with uncertain maintenance duration.

Various Applications of Purse-String Suture and Its Cosmetic Outcome in Cutaneous Surgical Defects.

Purse-string suture is a simple technique to reduce wound size and to achieve complete or partial closure of skin defects. To classify situations in which purse-string sutures can be utilized and to assess the long-term size reduction and cosmetic outcome of the final scar. Patients (93 from Severance hospital and 12 from Gangnam Severance hospital) in whom purse-string sutures were used between January 2015 and December 2019 were retrospectively reviewed. Wound site, final reconstruction method, repair duration, final wound size, and Vancouver scar scale were assessed. A total of 105 patients were reviewed. Lesions were located on the trunk (48 [45.7%]), limbs (32 [30.5%]), and face (25 [23.8%]). Mean ratio of wound length/primary defect length was 0.79±0.30. Multilayered purse-string suture showed the shortest duration from excision to final repair (p<0.001) and most effectively minimized the scar size (scar to defect size ratio 0.67±0.23, p=0.002). The average Vancouver scar scale measured at the latest follow-up visit at least 6 months postoperatively was 1.62, and the risk of hypertrophic scarring was 8.6%. There was no significant difference in the Vancouver scar scale and the risk of hypertrophic scarring between the different surgical method groups. Purse-string sutures can be utilized in many stages of reconstruction to effectively reduce scar size without compromising the final cosmetic outcome.

MTTF and availability of semi-Markov missions with non-identical generally distributed component lifetimes

We analyze mean time to failure and availability of systems that perform semi-Markov missions. The mission process is the minimal semi-Markov process associated with a Markov renewal process. Therefore, the successive phases of the mission follow a Markov chain, and the phase durations are generally distributed. The lifetimes of the non-identical components in the system are assumed to be generally distributed and are modeled using intrinsic aging concepts. Moreover, the lifetime parameters of the components and the configuration of the system change depending on the phases of the mission. We characterize the mean time to failure through solving a Poisson equation, and we analyze the system availability assuming that repair duration has a general distribution which is dependent on the phase of the mission during which the failure has occurred and on the deterioration level of the system.

Cataloging Of Unit Replacement Based On Long Run Repair Average Cost Rate (Acr) Based On Weibull Distribution Model

Large amounts of money are lost each year in the real-estate industry because of poor schedule and cost control, In Industry the investigated failure and repair pattern, reliabilities of generators, compressors, turbines, using simple statistical tools and simulation techniques. The repair duration is divided into the 1)Major repair 2)Minor repair ,In major repair having(repair hour greater than a threshold valve)and Minor repair having(repair hour less than (or)equal to threshold valve).This approach is mainly for Weibull distribution method. In Weibull analysis is a common method for failure analysis and reliability engineering used in a wide range of applications. In this paper, the applicability of Weibull analysis for evaluating and comparing the reliability of the schedule performance of multiple projects is presented, while the successive performance of multiple projects is presented ,while the successive repair times are increasing and are exposing to Weibull distribution ,under these assumptions ,an optimal replacement policy ‘T’ in which we replace the system ,when the repair time reaches T. It can be determined that an optimal repair replacement policy T* such that long run average cost and the corresponding optimal replacement policy T* can be determined analytically.

Collective multi agent deployment for wireless sensor network maintenance

In this paper, we study the problem of wireless sensor network (WSN) maintenance using a team of physical autonomous mobile agents. The agents are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and repair it. The team must constantly optimize its collective deployment to account for occupied agents. The objective is to define the optimal deployment and task allocation strategy, that minimize the solution cost. The solution cost is a linear combination of the weighted sensors’ downtime, the agents’ traveling distance, and penalties incurred due to unrepaired sensors within a certain time limit.Our proposed solution algorithms are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location.We empirically compare and analyze the performance of several proposed algorithms. The sensitivity of the algorithms’ performance to the following parameters is analyzed: agents to sensors ratio, sensors’ sparsity, frequency and distribution of failures, repair duration, repair capacity, and communication limitations. Our results demonstrate that: (i) cooperation enhances the team’s performance by orders of magnitude, (ii) k-Median based deployment algorithm provides up to 30% improvement in downtime, (iii) k-Center based deployment incurs 10% fewest penalties, and (iv) k-Centroid based deployment is most efficient in terms of minimizing the overall costs, with up to 21% lower cost than the next best algorithm.

Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk &amp;amp; compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.

Optimization Model for the Pavement Pothole Repair Problem Considering Consumable Resources

When heavy rain strikes Taiwan, it always results in cracks in road pavement, and damages arising from potholes. Tremendously compromising road safety, road users may have fatal accidents caused by untimely repair actions. The road maintenance department needs to take the responsibilities for road sections in the form of inspections and faces the decision about how to properly allocate available resources to repair pavement damages immediately. When performing pavement repair works, we need to consider the resource consumption behavior and explore the mechanism of replenishing resources and calculating the return time. Therefore, in order to help maintenance units to deal with consumable resource issues, this study proposes a novel approach to offer the mechanism of consumable resource calculation, which is difficult to solve through the traditional vehicle routing problem (VRP) approach. This proposed model treats the pothole repair problem as a resource-constrained project scheduling problem (RCPSP), which is capable of resolving such consumable resource considerations. The proposed model was developed by adopting constraint programming (CP) techniques. Research results showed that the proposed model is capable of providing the optimal decisions of pavement pothole repair tasks and also meets practical requirements to make appropriate adjustment, and helps the maintenance unit to shorten total repair duration and optimize resource assignment decisions of pavement maintenance objectives.

Resilient distribution network planning under the severe windstorms using a risk-based approach

This paper proposes a risk-based approach for planning resilient radial distribution networks to severe windstorms. The proposed approach is based on the concept of interval analysis, Relative Distance Measure (RDM) interval arithmetic, mixed integer linear programming, and risk analysis. It enables obtaining a number of different resilient network plans taking into account dependence among the following uncertain inputs: maximum wind speed, storm duration and its annual frequency of occurrence, fragility of network components, repair duration, forecasted load and generation from renewable generators. In order to improve the computational efficiency of the proposed approach a hybrid simulated annealing and mixed integer linear programming algorithm is introduced. The best resilient plan is selected by employing the Minimal risk (Minimax) criterion for measuring and managing risk. In this way, the proposed approach provides a decision-maker with a means of determining the resilient network plan that minimizes the risk of significant costs due to severe windstorms.

Reinforcement learning for combined production-maintenance and quality control of a manufacturing system with deterioration failures

This paper describes and examines thoroughly a stochastic production/inventory system that produces a single type of products. During the production process, the system is affected by several deterioration failures. It is restored to its initial and previous deterioration state by repair and maintenance activities. Both maintenance and repair duration are assumed as exponential random variables. Moreover, the quality of the manufactured products is assumed to be affected by the current deterioration level of the system. The aim of this paper is to find the optimal trade-off between conflicting performance metrics for the optimization of the total expected profit of the system. To tackle such optimization problems, researchers frequently employ Dynamic Programming. This method, though, is not appropriate for the addressed problem due to complexity reasons. To this end, a Reinforcement Learning-based approach is proposed in order to obtain the optimal joint production, maintenance and product quality control policies. To the authors’ knowledge, the proposed approach is novel and there are few examples of such implementation in the academic literature.

Post-disaster power system restoration planning considering sequence dependent repairing period

In this paper, a post-disaster restoration planning (PDRP) model for critical power system infrastructure is proposed. The model intends to find the optimal repair and activation schedule for damaged components so that the system load accommodation capability is maximized and the makespan of restoration process is minimized. The model takes into account the effect of adverse weather on the component repair process and a time-varying repair velocity function model is proposed to obtain the time-varying repair duration (TVRD) of the damaged component. The sequence-dependent repairing period (SDRP) of damaged components can be formed by combining TVRD and the transportation time of resources, and embedded into the PDRP model. The proposed model also allows the repair units to perform repair work on arbitrary damage component, not just the damage components in their routine service area. As a result, the maintenance efficiency is maximized. A two-stage bi-pheromone heuristic method is developed to efficiently solve the proposed PDRP model. The sequence position adherence information of each repair task and the relationship between two adjacent tasks are used in the search so that the solution time of the PDRP model is avoided from increasing exponentially with the problem size. The IEEE-118 and IEEE-30 bus test system are used for case studies to validate the proposed model and method. In addition, advantages of considering the sequence dependent repairing period are presented.

A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding

Resilience is an overarching concept that requires combined efforts from interdependent critical infrastructures to achieve. As the interdependence between the power system and the natural gas system grows, the roles of coordination in post-disaster repair will be unneglectable to improve the resilience of the two systems. In this paper, a combined repair crew dispatch problem for the interdependent power and natural gas systems is proposed. The repair schedule of the two systems is coordinated and co-optimized. Both power system topology reconfiguration and intentional DG islanding are modeled as operational measures to further improve the resilience of the interdependent systems. Case studies validate the effectiveness of the proposed method in reducing load shedding and repair duration, and prove that the interdependence has a significant impact on the repair sequence and crew coordination.

System loss assessment of bridge networks accounting for multi-hazard interactions

This paper details an integrated method for the multi-hazard risk assessment of road infrastructure systems exposed to potential earthquake and flood events. A harmonisation effort is required to reconcile bridge fragility models and damage scales from different hazard types: this is achieved by the derivation of probabilistic functionality curves, which express the probability of reaching or exceeding a loss level given the seismic intensity measure. Such probabilistic tools are essential for the loss assessment of infrastructure systems, since they directly provide the functionality losses instead of the physical damage states. Multi-hazard interactions at the vulnerability level are ensured by the functionality loss curves, which result from the assembly of hazard-specific fragility curves for local damage mechanisms. At the hazard level, the potential overlap between earthquake and flood events is represented by a time window during which the effects of one hazard type on the infrastructure may still be present: the value of this temporal parameter is based on the repair duration estimates provided by the functionality loss curves. The proposed framework is implemented through Bayesian Networks, thus enabling the propagation of uncertainties and the computation of joint probabilities. The procedure is demonstrated on a bridge example and a hypothetical road network.

Module level reliability performance evaluation of digital reactor protection system considering the repair and common cause failure

The Reactor Protection System (RPS) is designed and installed in the nuclear power plants (NPPs) to ensure both safety and economy. Nowadays the RPS adopts the digital techniques which consist of different digital modules. Therefore, this paper focuses on evaluating the reliability performance of the digital RPS using the Colored Petri Net (CPN) considering the module repair time whenever it fails and the Common Cause Failure (CCF). The module repair is considered as it takes some time to repair or replace the failed module and during the repair duration the digital RPS is operated in the degraded configuration and the common cause failure would severely impact the system in the event of occurrence. By studying the failure phenomenon and mechanism, the random probability shock model is adopted for CCF. Using the proposed model, the Monte Carlo simulation is carried out. Consequently, the indicators such as Mean Time To Repair (MTTR), Mean Time Between Failures (MTBF), Probability of Failure on Demand (PFD) and Probability of Spurious Trip (PST) are calculated. Following main conclusions are drawn i.e., i) the CCF is the main contribution to the PFD and PST. So the countermeasure for the CCF must be designed for the digital RPS; ii) the CCF has no effect on the MTTFF, MTBF, MTTR and subsystem unavailability; iii) the failure detection time has adverse effect on the system. Therefore, the digital system should shorten the detection time or decrease the coverage for the failures that take long time to be detected.

MOSOA-Based Multiobjective Design of Power Distribution Systems

This paper presents a multiobjective (MO) evolutionary algorithm for solving a contingency-based MO design of power distribution system (PDS) by extending the original and powerful metaheuristic approach based on a MO seeker optimization algorithm (MOSOA). Normally, reliability is a major concern in existing PDS planning, as estimation of failure rates and fault repair duration of the feeder branches is difficult in practice. The proposed planning methodology uses a contingency-load-loss index for reliability evaluation, which is independent of the failure rate and fault repair duration of the feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability and efficiency of the distribution system. The proposed algorithm generates a set of nondominated solutions by the simultaneous optimization of two conflicting objectives (economic cost and overall system reliability) using Pareto-optimality-based tradeoff analysis. The performance of the proposed approach is assessed and illustrated on a 54-bus distribution system, considering real-time design practices and meeting the additional requirements that the designer imposes. The information gained from the Pareto-optimal solution is shown to be useful for final decision making of a PDS. Furthermore, a qualitative comparison is made with the nondominated sorting genetic algorithm-II, showing the efficacy of the proposed planning approach.

Reliability model of the digital reactor protection system considering the repair time and common cause failure

ABSTRACTReliability of the digital reactor protection system (RPS) is intensively researched as it is designed and installed to ensure the safety and economy which can be measured respectively by the probability of failure on demand (PFD) and probability of spurious trip (PST). Meanwhile, by analyzing the failure modes of the digital RPS, the failure on demand and spurious trip are the two main modes that should be evaluated for the reliability of digital RPS. Therefore, this paper develops the PFD and PST calculation formulas considering the module repair time as the repair takes some time, and during the repair duration, the digital system is operated in the degraded configuration and the common cause failure (CCF) which would severely impact the system in the event of occurrence. Considering the failure phenomenon of the digital RPS, the binomial failure rate (BFR) model is adopted for CCF. And the fault-tolerance techniques and their fault coverage are considered when calculating the PFD and PST. The quantitative results show that, in the example, CCF dominates the PFD while CCF is one of the major factors that result in PST but the main contributor is the independent failure. Also it can be concluded that the discovery time for the undetected failures dominates the PFD and PST when it costs long time to discover the failures even though the uncovered failures are very few. Thus, the failures should be covered by the fault-tolerance techniques as much as possible when designing the digital RPS.