A Study on Maintenance Reliability Allocation of Urban Transit Brake System Using Hybrid Neuro-Genetic Technique

Chul-Ho Bae,Yul Chu,Hyun-Jun Kim,Jung-Hwan Lee,Myung-Won Suh 대한기계학회 2007 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.21 No.1

For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. In this paper, the concept of system reliability introduces and optimizes as the key of reasonable maintenance strategies. This study aims at optimizing component’s reliability that satisfies the target reliability of brake system in the urban transit. First of all, constructed reliability evaluation system is used to predict and analyze reliability. This data is used for the optimization. To identify component reliability in a system, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between component reliability (input) and system reliability (output) of a structural system. The inverse problem can be formulated by using neural network. Genetic algorithm is used to find the minimum square error. Finally, this paper presents reasonable maintenance cycle of urban transit brake system by using optimal system reliability.

STUDY OF RCM-BASED MAINTENANCE PLANNING FOR COMPLEX STRUCTURES USING SOFT COMPUTING TECHNIQUE

손영탁,김배영,박기준,이호용,서명원,김현준 한국자동차공학회 2009 International journal of automotive technology Vol.10 No.5

To guarantee the efficiency of maintenance strategies for a complex structure, safety and cost limitations must be considered. This research introduces RCM-based (Reliability Centered Maintenance) life cycle optimization for reasonable maintenance. The design variable is the reliability of each part, which consists of a complex structure, while the objective is to minimize the total cost function in order to maintain the system within the desired system reliability. This research constructs the cost function that can reflect the current operating condition and maintenance characteristics of individual parts by generating essential cost factors. To identify the optimal reliability of each component in a system, this paper uses a Neuro-Evolutionary technique. Additionally, this research analyzes the reliability growth of a system by using the AMSAA (Army Material Systems Analysis Activity) model to estimate the failure rate of each part. The MTBF (Mean Time Between Failure) and the failure rate of the whole system, which is responding to the individual parts, are estimated based on the history data by using neural networks. Finally, this paper presents the optimal life cycle of a complex structure by applying the optimal reliability and the estimated MTBF to the RAMS (Reliability, Availability, Maintainability, and Safety) algorithm. To guarantee the efficiency of maintenance strategies for a complex structure, safety and cost limitations must be considered. This research introduces RCM-based (Reliability Centered Maintenance) life cycle optimization for reasonable maintenance. The design variable is the reliability of each part, which consists of a complex structure, while the objective is to minimize the total cost function in order to maintain the system within the desired system reliability. This research constructs the cost function that can reflect the current operating condition and maintenance characteristics of individual parts by generating essential cost factors. To identify the optimal reliability of each component in a system, this paper uses a Neuro-Evolutionary technique. Additionally, this research analyzes the reliability growth of a system by using the AMSAA (Army Material Systems Analysis Activity) model to estimate the failure rate of each part. The MTBF (Mean Time Between Failure) and the failure rate of the whole system, which is responding to the individual parts, are estimated based on the history data by using neural networks. Finally, this paper presents the optimal life cycle of a complex structure by applying the optimal reliability and the estimated MTBF to the RAMS (Reliability, Availability, Maintainability, and Safety) algorithm.

발전설비용 회전기계의 소급적 신뢰도중심정비 사례 연구

김희수(Heesoo Kim),노영진(Youngjin Roh),손정욱(Jungwook Son),임강민(Gangmin Lim),김선화(Seonhwa Kim) 대한기계학회 2021 대한기계학회 논문집. Transactions of the KSME. C, 산업기술과 혁신 Vol.9 No.1

본 논문에서는 소급적 신뢰도중심정비의 방법론을 회전기기의 정비 이력 데이터를 통하여 최적의 정비 주기를 도출한 사례를 소개하였다. 소급적 신뢰도중심정비는 대상 설비의 정비 현황을 분석하고, 설비의 신뢰도를 향상시키기 위하여 고장 라이브러리의 FMEA 결과를 기반으로 누락된 정비를 차기 정비전략에 적용하는 분석 방법이다. 본 연구에서 사례 연구로 활용한 데이터의 대상 설비는 펌프, 팬, 모터이며 사용된 정비 이력은 계획예방정비 정보이다. 사례 연구를 통하여 대상 설비에 대한 MTBF, MTTR, MTTI 의 핵심 지표가 도출되었으며, 이 지표는 정비 전략의 정비 주기를 결정하는 의사결정 알고리즘 변수로 활용되었다. 발전설비의 보수적인 예방정비로 인하여 고장 데이터의 확보가 어려우므로 본 연구에서는 선택적 의사 결정 알고리즘 적용이라는 개념을 도입하여 데이터를 분석하였다. 의사결정 알고리즘은 가용도 기준, 정비 시간 기준, 총 정비 비용 기준 알고리즘으로 구성되어 있으며, 대상 설비의 정비 이력에 따라 최적의 알고리즘을 적용하여 데이터 분석하였다. 또한 각각의 알고리즘은 Weibull Analysis 를 통한 주요 변수를 산출한 후 최적의 정비 주기 또는 검사 주기를 도출하였다. In this paper, we introduced a case of deriving an optimal maintenance cycle through retrospective reliability-based maintenance methodology through maintenance history data of rotating equipment. Hybrid reliability centered maintenance is an analysis method that analyzes the maintenance status of the target equipment and applies the missing maintenance to the next maintenance strategy based on the FMEA results of the failure library to improve the reliability of the equipment. The target machinery of the data used as a case study in this study are pumps, fans and motors, and the maintenance history used is information on planned preventive maintenance. Through the case study, the key indicators of MTBF, MTTR, and MTTI for the target equipment were derived, and this indicator was used as a decision algorithm variable to determine the maintenance cycle of the maintenance strategy. Because it is difficult to secure fault data due to the conservative preventive maintenance of machinery in power generation, this study analyzed the data by introducing the concept of applying an optional decision algorithm. The decision-making algorithm is composed of 3 algorithms (the availability, maintenance time and total maintenance cost). Data is analyzed by applying the optimal algorithm according to the maintenance history of the target machinery. In addition, each algorithm calculated the main variables through Weibull Analysis and then derived the optimal maintenance or inspection cycle.

야전운용제원과 신뢰도중심정비에 기반한 장갑차 암내장형 유기압 현수장치(ISU) 정비계획 최적화

이현석,이두열 한국신뢰성학회 2024 신뢰성응용연구 Vol.24 No.1

Purpose: This study utilizes reliability-centered maintenance (RCM) to analyze the in-armed suspension unit (ISU) of armored vehicles. The aim is to propose a maintenance schedule that minimizes maintenance costs based on the RCM analysis. Methods: Fault tree analysis was performed to define the failure modes of the armored vehicle ISU system. The life distribution parameters for each part of the ISU were estimated using field operation data. Subsequently, the probability of failure and the number of expected failure events of a reference group comprising 100 armored vehicles were calculated by considering operation time. Lastly, a parametric study was conducted to determine the optimal maintenance schedule. Results: This study utilizes RCM to present the root cause of ISU failure and its consequences. Considering the targeted operational time, the optimal maintenance schedule was 90 h of operation for 100 armored vehicles. Performing maintenance at this interval resulted in the lowest maintenance costs. Conclusion: Introducing maintenance after every 90 h of operation instead of the current corrective maintenance scheme is expected to reduce the frequency of unexpected failures and minimize the costs associated with unscheduled maintenance. This method can be applied to other equipment subject to gradual deterioration to determine the optimal maintenance schedule.

Application of Particle Swarm Optimization to the Reliability Centered Maintenance Method for Transmission Systems

Jae-Haeng Heo,Jae-Kun Lyu,Mun-Kyeom Kim,Jong-Keun Park 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.6

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.

발전설비용 회전기계의 소급적 신뢰도중심정비 사례 연구

김희수,노영진,손정욱,임강민,김선화 대한기계학회 2021 기술과 교육 Vol.9 No.1

본 논문에서는 소급적 신뢰도중심정비의 방법론을 회전기기의 정비 이력 데이터를 통하여 최적의 정비 주기를 도출한 사례를 소개하였다. 소급적 신뢰도중심정비는 대상 설비의 정비 현황을 분석하고, 설비의 신뢰도를 향상시키기 위하여 고장 라이브러리의 FMEA 결과를 기반으로 누락된 정비를 차기 정비 전략에 적용하는 분석 방법이다. 본 연구에서 사례 연구로 활용한 데이터의 대상 설비는 펌프, 팬, 모터이며 사용된 정비 이력은 계획예방정비 정보이다. 사례 연구를 통하여 대상 설비에 대한 MTBF, MTTR, MTTI 의 핵심 지표가 도출되었으며, 이 지표는 정비 전략의 정비 주기를 결정하는 의사결정 알고리즘 변수로 활용되었다. 발전설비의 보수적인 예방정비로 인하여 고장 데이터의 확보가 어려우므로 본 연구 에서는 선택적 의사 결정 알고리즘 적용이라는 개념을 도입하여 데이터를 분석하였다. 의사결정 알고리 즘은 가용도 기준, 정비 시간 기준, 총 정비 비용 기준 알고리즘으로 구성되어 있으며, 대상 설비의 정비 이력에 따라 최적의 알고리즘을 적용하여 데이터 분석하였다. 또한 각각의 알고리즘은 Weibull Analysis 를 통한 주요 변수를 산출한 후 최적의 정비 주기 또는 검사 주기를 도출하였다. In this paper, we introduced a case of deriving an optimal maintenance cycle through retrospective reliability-based maintenance methodology through maintenance history data of rotating equipment. Hybrid reliability centered maintenance is an analysis method that analyzes the maintenance status of the target equipment and applies the missing maintenance to the next maintenance strategy based on the FMEA results of the failure library to improve the reliability of the equipment. The target machinery of the data used as a case study in this study are pumps, fans and motors, and the maintenance history used is information on planned preventive maintenance. Through the case study, the key indicators of MTBF, MTTR, and MTTI for the target equipment were derived, and this indicator was used as a decision algorithm variable to determine the maintenance cycle of the maintenance strategy. Because it is difficult to secure fault data due to the conservative preventive maintenance of machinery in power generation, this study analyzed the data by introducing the concept of applying an optional decision algorithm. The decision-making algorithm is composed of 3 algorithms (the availability, maintenance time and total maintenance cost). Data is analyzed by applying the optimal algorithm according to the maintenance history of the target machinery. In addition, each algorithm calculated the main variables through Weibull Analysis and then derived the optimal maintenance or inspection cycle.

事務所建物 空調設備의 豫防保全 最適點檢周期가 信賴性向上 및 期待利益에 미치는 영향

곽노열,박병윤,손장열 대한건축학회 2003 대한건축학회논문집 Vol.19 No.5

In this study, using Monte Carlo simulation about inspection model applied practically in air-conditioning facilities of office building, the optimal preventive maintenance inspection period is suggested to improve the reliability of the units, and effect on expected profit of optimal preventive maintenance inspection period is computed. The main results of this study are summarized as follows: 1) Simulation program for sensitivity analysis and optimal preventive maintenance inspection period is made, in order to execute precisely sensitivity analysis of maintenance factors, by using Monte Carlo simulation's flow chart and compute optimal preventive maintenance inspection period. 2) Using models for the relationship between reliability and inspection frequency per unit time and model between inspection and expected profit of preventive maintenance by CBM, the optimal preventive maintenance inspection period of air-conditioning facilities is computed, and it is revealed that the range of that is 90∼175 hr and, correlation curve of the original MTBF and optimal preventive maintenance inspection period by CBM is derived. 3) And effect on expected profit of preventive maintenance's activity by optimal preventive maintenance inspection period is computed. It is found that the larger the original MTBF, like MTBF of centrifugal compression chiller and AHU is, and the smaller the expected profit increased, and the smaller the original MTBF, like MTBF of boiler, absorption chiller and turbo case cooling tower is, and larger the expected profit increased.

Reliability-Centered Maintenance Strategy for Redundant Power Networks Using the Cut Set Method

Lee Ji Woo,Kim Seung Wan 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.3

In this study, we present an effi cient maintenance strategy for securing stable power supplies in power facilities. In general, industrial customers that may face large outage costs from power supply interruptions prepare highly reliable systems with redundant network topologies. When establishing a maintenance schedule for an industrial power system, the condition of the equipment should be inspected fi rst. However, frequent inspections may result in excessive inspection costs, and infrequent inspections may reduce the reliability of the system. Therefore, it is necessary to determine an optimal maintenance and inspection interval that balances the demand for reliable electricity and the need to limit maintenance and inspection costs. Accordingly, considering the topology of the equipment in terms of the system as a whole, a reliability-centered maintenance algorithm was derived to determine the specifi c combinations of equipment conditions that cause power outages. Generally, the more redundant the elements in the system were, the less the load experienced power outages. We also examined the unavailability, which is an indicator that predicts the probability of the load becoming unpowered. Finally, we obtained an optimal inspection interval and maintenance decision table for events that occur in practice.

배전계통 기기 유지보수를 위한 RCM 모델

문종필(Jong-Fil Moon),손진근(Jin-Geun Shon) 대한전기학회 2009 전기학회논문지 P Vol.58 No.4

With the implementation of electric power industry reform, the utilities are looking for effective ways to improve the economic efficiency. One area in particular, the equipment maintenance, is being scrutinized for reducing costs while keeping a reasonable level of the reliability in the overall system. Here the conventional RCM requires the tradeoff between the upfront maintenance costs and the potential costs of losing loads. In this paper we describe the issues related to applying so-called the “Reliability-centered Maintenance” (RCM) method in managing electric power distribution equipment. The RCM method is especially useful as it explicitly incorporates the cost-tradeoff of interest, i.e. the upfront maintenance costs and the potential interruption costs, in determining which equipment to be maintained and how often. In comparison, the “Time-based Maintenance” (TBM) method, the traditional method widely used, only takes the lifetime of equipment into consideration. In this paper, the modified Markov model for maintenance is developed. First, the existing Markov model for maintenance is explained and analyzed about transformer and circuit breaker, so on. Second, developed model is introduced and described. This model has two different points compared with existing model: TVFR and nonlinear customer interruption cost (CIC). That is, normal stage at the middle of bathtub curve has not CFR but the gradual increasing failure rate and the unit cost of CIC is increasing as the interruption time is increasing. The results of case studies represent the optimal maintenance interval to maintain the equipment with minimum costs. A numerical example is presented for illustration purposes.

태양광 시스템의 신뢰성 중심 적응형 유지보수 계획

한성호,남태양,김병기,문원식,김재철 대한전기학회 2022 전기학회논문지 P Vol.71 No.4

Recently, as the supply of photovoltaic systems has increased, photovoltaic facilities with long operation days are increasing. As the number of operating days of a photovoltaic facility increases, the failure rate increases. Failures of these facilities affect the reliability of the connected power system. In addition, photovoltaic generation operators suffer cost losses when supply is disrupted. Therefore, a proper maintenance plan for photovoltaic facilities is required. This paper established an adaptive maintenance plan for photovoltaic systems focusing on reliability. Reliability was analyzed through the life evaluation of each piece of equipment in the photovoltaic system, and the system reliability index was calculated.