Earticle

Failure mode and effects analysis(FMEA) is popular approach applied to examine potential failures in equipment designs and maintenance of equipments. Risk Priority Number(RPN) is used as an index for the criticality of fault modes in FMEA and RCM(reliability centered maintenance). Traditional RPN approach does not have much credit as a index for the criticality because it does not reflect their experience and the governing logic is apart from their knowledge. Multiple Criteria Decision Method(MCDM) is a proven approach applied to evaluate multiple conflicting criteria in decision making. MCDM can be applied as a tool for RPN evaluation. This study was carried out to investigate application of Analytical Hierarchy Process(AHP) in evaluating RPN.

전력 설비의 기능 저하와 노후화는 전력 시스템의 신뢰성과 안정성에 치명적인 영향을 미치므로, 신속하게 효과적인 유지보수 전략을 수립하는 것이 중요하다. 기존의 정수 계획법 기반의 최적화 전략은 전역 최적해를 보장하지만, 대규모 시스템에서 지수적으로 증가하는 시간 복잡도로 인해 응용 범위가 제한된다. 본 논문은 최적해에 준하는 유지보수 전략을 수 초 내에 도출하기 위해, 전력 설비 유지보수 최적화 문제에서 일반적으로 사용하는 SOS1(Special Ordered Set Type 1) 구조를 활용한 SAIR(SOS1-Aware Iterative Rounding Framework) 기법을 제안한다. 제안하는 SAIR은 결정 변수의 이진 제약 조건을 완화하여 유지보수 전략 최적화 문제를 선형 계획법 문제로 변환하고, SOS1 구조를 활용하여 탐색 공간을 설비 단위로 축소한다. 또한, 최소 엔트로피 기반 설비 선택 및 예산 인지 기반 유지보수 전략 선택 기법을 통해 전력 설비 유지보수 전략 문제에서 준최적해의 품질을 체계적으로 향상시킨다. 합성 데이터 기반 벤치마크 환경에서 다양한 규모의 시스템 복잡도에 대해 실험한 결과, 제안하는 SAIR은 전역 최적해 대비 0.09%의 목적함수 격차만을 보이며 전역 최적해 수준의 유지보수 전략을 도출하는 동시에 계산 시간을 평균 1512배 단축하여(평균 0.2492초) 계산 효율성을 크게 높일 수 있음을 보였다. 특히, 단순 휴리스틱 기반의 유지보수 계획 기법이 빈번하게 제약조건을 위배하여 무효한 해를 도출하는 것과 달리, SAIR은 모든 실험에서 유효한 전략을 도출하여 제안하는 기법의 유효성을 확인하였다.

Degradation and aging of power facilities critically impact the reliability and stability of power systems, necessitating the prompt establishment of effective maintenance strategies. Conventional optimization strategies based on integer programming guarantee globally optimal solutions, but their exponentially increasing time complexity in large-scale systems limits their applicability. To derive maintenance strategies comparable to the optimal solution within seconds, this paper proposes the SOS1-Aware Iterative Rounding Framework (SAIR), which leverages the Special Ordered Set Type 1 (SOS1) structure commonly used in the power facility maintenance optimization problem. The proposed SAIR transforms the maintenance strategy optimization problem into a linear programming problem by relaxing the binary constraints of the decision variables and utilizes the SOS1 structure to reduce the search space at the facility level. Furthermore, it systematically enhances the quality of near-optimal solutions in the power facility maintenance strategy problem through minimum entropy-based facility selection and budget-aware maintenance strategy selection techniques. In benchmark experiments using synthetic data across various system complexity environments, the proposed SAIR significantly enhances computational efficiency, achieving a maintenance strategy quality comparable to the globally optimal solution with an objective function gap of only 0.09%, while simultaneously reducing computation time by a factor of 1512 on average (0.2492 seconds on average). Notably, unlike simple heuristic-based maintenance planning methods that frequently violate constraints and yield invalid solutions, SAIR derived valid strategies in all experiments, thereby confirming the effectiveness of the proposed technique.

This paper considers a condition-based maintenance (i.e. CBM) model for the hydro generating unit in the deregulated power system. As the downtime cost of the generating unit varies according to the time-varying electricity price, the economic dependence among the critical component of the generating unit is dynamic. In this paper, the proposed Dynamic-Economic-Dependence and proportional hazards model based CBM policy considers the dynamic economic dependence among components as well as the reliability and the monitoring information of different critical components. An example for the hydro generating unit is presented to verify the effectiveness of the proposed condition based maintenance policy.

In order for the optimal solution of generators’ annual maintenance scheduling to be applicable to the actual power system it is crucial to incorporate the constraints related to the equivalent operation hours (EOHs) in the optimization model. However, most of the existing researches on the optimal maintenance scheduling are based on the assumption that the maintenances are to be performed periodically regardless of the operation hours. It is mainly because the computation time to calculate EOHs increases exponentially as the number of generators becomes larger. In this paper an efficient algorithm based on demand grouping method is proposed to calculate the approximate EOHs in an acceptable computation time. The method to calculate the approximate EOHs is incorporated into the optimization model for the maintenance scheduling with consideration on the EOHs of generators. The proposed method is successfully applied to the actual Korean power system and shows significant improvement when compared to the result of the maintenance scheduling algorithm without consideration on EOHs.

This paper considers an imperfect repair model for which the repairable system is maintained preventively at periodic times and is replaced by a new system when a predetermined number of preventive maintenance has been applied. our main objective of this is to determine the optimal number of preventive maintenances before the system is replaced and the optimal length of interval between two consecutive preventive maintenances under a new repair model which is referred to as an ineffective preventive maintenance. Such a model assumes a periodic preventive maintenance in which the system is effectively maintained with a certain probability. Otherwise the system is not improved at all after each maintenance and thus the failure rate remains the same as before. The criteria to determine the optimal number of preventive maintenances and length of period is the expected cost rate per unit time for an infinite time span. We give the explicit expressions for the expected cost rate per unit time. Some numerical examples are presented for illustrative purposes.

Electric power transmission utilities make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability. The development of maintenance strategies for aged components is one of the more effective ways to achieve this goal. The reliability centered approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs incurred by reliability losses. This paper discusses the application of the Particle Swarm Optimization (PSO) technique used to find the optimal maintenance strategy for a transmission component in order to achieve the minimum total expected cost composed of Generation Cost (GC), Maintenance Cost (MC), Repair Cost (RC) and Outage Cost (OC). Three components of a transmission system are considered: overhead lines, underground cables and insulators are considered. In regards to aged and aging component, a component state model that uses a modified Markov chain is proposed. A simulation has been performed on an IEEE 9-bus system. The results from this simulation are quite encouraging, and then the proposed approach will be useful in practical maintenance scheduling.

This work presents fuel-optimal altitude maintenance of Low-Earth-Orbit (LEO) spacecrafts experiencing non-negligible air drag and J2 perturbation. A pseudospectral (direct) method is first applied to roughly estimate an optimal fuel consumption strategy, which is employed as an initial guess to precisely determine itself. Based on the physical specifications of KOrea Multi-Purpose SATellite-2 (KOMPSAT-2), a Korean artificial satellite, numerical simulations show that a satellite ascends with full thrust at the early stage of the maneuver period and then descends with null thrust. While the thrust profile is presumably bang-off, it is difficult to precisely determine the switching time by using a pseudospectral method only. This is expected, since the optimal switching epoch does not coincide with one of the collocation points prescribed by the pseudospectral method, in general. As an attempt to precisely determine the switching time and the associated optimal thrust history, a shooting (indirect) method is then employed with the initial guess being obtained through the pseudospectral method. This hybrid process allows the determination of the optimal fuel consumption for LEO spacecrafts and their thrust profiles efficiently and precisely.

In this paper, we propose a time-series generation methodology using a generative adversarial network (GAN) for long-term prediction of wind and sea conditions, which are information necessary for operations and maintenance (O&M) planning and optimal plans for offshore wind farms. It is a “Conditional TimeGAN” that is able to control time-series data with monthly conditions while maintaining a time dependency between time-series. For the generated time-series data, the similarity of the statistical distribution by direction was confirmed through wave and wind rose diagram visualization. It was also found that the statistical distribution and feature correlation between the real data and the generated time-series data was similar through PCA, t-SNE, and heat map visualization algorithms. The proposed time-series generation methodology can be applied to monthly or annual marine weather prediction including probabilistic correlations between various features (wind speed, wind direction, wave height, wave direction, wave period and their time-series characteristics). It is expected that it will be able to provide an optimal plan for the maintenance and optimization of offshore wind farms based on more accurate long-term predictions of sea and wind conditions by using the proposed model.

Using the life cycle cost concept, optimum maintenance and retrofit planning for reliable seismic performance is suggested the overall life cycle cost to be minimized including the initial cost, the costs of inspection, repair, and failure. Limit states of the bridges are defined. And failure probabilities are computed through crossing theory. The effect of maintenance and retrofit is represented using the probability of damage detection and event tree analysis. Optimization of maintenance and retrofit planning method proposed from this research was applied to numerical examples. The analysis incorporates the acceleration and site conditions prescribed in the code, and the quality of inspection methods.

현대 무기체계는 하드웨어 중심 구조에서 소프트웨어 중심 체계로 전환되면서, 소프트웨어 유지보수의 중요성이 전략적 차원에서 주목받고 있다. 무기체계 소프트웨어 유지보수 일정 최적화 문제는 기술자의 이동 경로와 작업 일정을 동시에 고려해야 하는 복합 최적화 문제로서 NP-Hard 문제에 해당한다. 본 연구는 이러한 문제를 해결하기 위하여 기존의 탐욕 알고리즘(Greedy Algorithm)과 이웃해 탐색(Neighborhood Search) 기법의 한계를 보완하는 블록 기반 휴리스틱(Block-Based Heuristic, BBH) 알고리즘을 제안한다. 연구 방법으로는 무기체계 소프트웨어 유지보수 일정계획 문제를 기술자 경로 및 일정 문제(Technician Routing and Scheduling Problem, TRSP)로 정형화하여 수학적 모형을 구성한 후, 제안한 알고리즘을 적용한 모의실험(Simulation)을 수행하였다. 실험 결과, 기존 연구와 동일한 데이터 구조를 적용한 비교 실험에서는 기존 기법 대비 목적함수 값이 9.1%에서 13.1%까지 개선되는 것으로 나타났으며, 실제 무기체계 유지보수 일정 데이터를 적용한 실험에서는 초기해 대비 최선해가 12.6%에서 최대 64.6%까지 개선되는 성과를 확인하였다. 본 연구의 학문적 기여는 TRSP 문제에 블록 기반 탐색구조를 도입함으로써 탐색 공간을 효율적으로 분할하고 해의 수렴 안정성을 향상한 데 있다. 또한 실무적으로는 종합 군수 지원체계 등 무기체계 유지보수 환경에 적용함으로써 제한된 인력과 자원하에서 유지보수 운영 효율성을 제고하고, 무기체계 가동률 향상에 이바지할 수 있을 것으로 기대된다.

As modern weapon systems have transitioned from hardware-centered architectures to software-intensive systems, the strategic importance of software maintenance has become increasingly prominent. The weapon system software maintenance scheduling problem is a complex optimization problem that simultaneously considers technicians’ routing and task scheduling and is classified as an NP-hard problem. To address this challenge, this study proposes a Block-Based Heuristic (BBH) algorithm that overcomes the limitations of conventional Greedy Algorithms and Neighborhood Search methods. The research formulates the weapon system software maintenance scheduling problem as a Technician Routing and Scheduling Problem (TRSP) and develops a mathematical optimization model. Based on this formulation, the proposed BBH algorithm is designed and evaluated through simulation experiments. The experimental results using the same data structure as prior studies demonstrate that the proposed approach improves the objective function value by 9.1% to 13.1% compared with existing methods. Furthermore, when applied to real-world weapon system software maintenance scheduling data, the proposed algorithm achieves improvements ranging from 12.6% to a maximum of 64.6% over the initial solutions. The primary academic contribution of this study lies in extending the TRSP by introducing a block-based search structure, which enables efficient partitioning of the search space and enhances solution convergence stability. From a practical perspective, the proposed approach can be applied to integrated logistics support systems, contributing to improved maintenance operational efficiency and increased availability of weapon systems under constrained personnel and resource environments.

The optimal design was performed for the bridge superstructure composed of steel box girders and concrete deck considering life cycle cost. The service life of the superstructure was estimated, after load carry capacity curves for steel girder and concrete deck were derived on the basis of condition grade curves and maintenance histories. The object function was determined as life cycle cost, including initial cost, total maintenance cost, disposal cost and user cost, for a period of the estimated service life. The optimal design of the superstructure was performed for the various service lifes. The annual costs were used to compare calculated results and to get the most economical design. Also this paper presents reasonable idea for the use of user cost with uncertainty.

This study is a study on the decision of depot maintenance plan using an optimization model based on systems engineering. The existing decision-making model is a decision-making model based on qualitative methods, and unreasonable decisions have been made due to decision-making based on the policy decisions of related organizations, subjective evaluation items, and the number of favorable and unfavorable items for each evaluation item without weighting. However, to overcome these limitations, a quantitative decision is made to determine the depot maintenance cycle using the cost equivalent cost method as a way to determine depot maintenance plans based on quantitative evaluation indicators based on purpose and efficiency, and to determine the location of depot maintenance through cost-effectiveness analysis. As a decision method, it was made possible to increase objectivity and efficiency when deciding on depot maintenance plans for weapon systems currently in operation in the military or to be developed in the future. Therefore, this study was applied to the eight major weapon systems currently in operation in the military to enable effective decision-making. was provided to support.

최근 철도운영기관은 동력집중식 전동차보다 높은 가감속 성능, 총 동력 및 공간활용성 등의 장점으로 인하여 동력분산식을 선호한다. 이러한 추세에 따라 동력집중식 철도차량 정비기지는 동력분산식 철도차량의 신규 도입을 계획하므로 기존 차량기지의 리모델링을 요구한다. 이러한 리모델링은 기존 집중식 정비기지에 분산식 철도차량을 혼용하도록 추가적인 비용, 운용인력 및 인프라를 건설하는 것을 의미한다. 본 논문에서는 동력집중식 철도차량 정비기지에서 동력분산식 철도차량을 함께 유지보수할 수 있는 복합기능 차량 정비기지를 건설하기 위하여 소요 정비용량을 추정하였다. 또한 본 차량 정비기지 건설비용과 시간의 줄이기 위하여 복합 기능에 적합한 정비선 수에 대한 최적화를 수행하였다.

Recently, railroad corporation prefer EMU because of their advantages such as higher acceleration/deceleration performance, total power and space utilization compared to power concentrated train. According to this trend, the depot for power concentrated railway vehicles require remodeling of existing that because they plan to introduce new EMU. This remodeling means adding cost and operating manpower and constructing infrastructure to mix EMU with the existing depot. In this paper, the required maintenance capacity was estimated to construct a multi-function depot that can maintain EMU together at existing depot. In addition, in order to reduce the construction cost and time of this depot, optimization of the number of lines suitable for multi-funciton was performed.

Planned maintenance reduces the occurrence of excessive lead times during production or transportation, and prevents large-scale accidents during equipment operation. The probability of failure(Failure-Function) during total life cycle should be considered to establish a maintenance schedule. In this study, we propose an Interval-Optimization- Maintenance. This divides the total life cycle according to the failure probability based on the Failure-Function, and performs an optimized Period-Based-Maintenance in the divided interval. The proposed model has both the convenience of scheduling and prevention of failure. And, the proposed model is a realistic model that can be applied to reality. Failure-Function was derived using the data of the Naval ship and the hierarchical Bayesian estimation method. The results of this study can be used in all industries operating equipment. It is possible to optimize the planned maintenance interval by estimating the Failure-Function of the industrial group and constructing a maintenance model.

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

본 논문에서는 비재생보증(non-renewing warranty)이 종료된 이후의 주기적인 예방보전모형(periodic preventive maintenance)이 고려된다. 만약, 비재생보증기간이 종료된 이후에 시스템에 고장이 발생하면 최소수리를 수행한다. 최적의 예방보전정책을 결정하기 위한 기준으로 기대비용(expected cost)과 기대비가동시간(expected downtime)에 근거한 총밸류함수(overall value function)가 사용된다. 그래서 시스템을 유지하기 위한 비용과 비가동시간이 주어져 있을 때 주기적인 예방보전모형에 대한 단위시간당 기대비용과 단위시간당 기대비가동시간을 구한다. 그리고 시스템의 고장시간이 와이블분포를 할 때 수치적 예를 통해서 이를 설명한다.

This paper considers a periodic preventive maintenance model following the expiration of non-renewing warranty. If the system fails after the non-renewing warranty period is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal preventive maintenance policy is the overall value function based on the expected cost and the expected downtime. Thus, we obtain the expected cost rate per unit time and the expected downtime per unit time for the periodic PM model when the cost and downtime structures of maintaining the system are given. The numerical examples when the failure time follows a Weibull distribution are presented for illustrative purpose.