• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.105-117
• /
• 2003

The purpose of this research aimed at performing the easy design. and also the easy on-the-job application or the maintenance interval determination methodology by presenting the determining model or the optimizing maintenance interval in TBM for the preventive maintenance or facility TBM(time-based maintenance) as the preventive maintenance requires the adequate determination or the maintenance interval. The maintenance interval or TBM shall be applied differently for the each interval such as He patrol inspection, maintenance, overhaul inspection. exchange. And it is based on the composition level of equipment. The already informed theories or interval determination methodology for the patrol inspection. repair. and overhaul inspection are difficult for adopting because or the several restriction problems in applying the maintenance schemes as the theory So, the model for determining the optimizing exchange interval or part, maintenance interval of auxiliary machine, unit equipment etc. was presented to apply in the maintenance easily and appropriately.

• KNOM Review
• /
• v.24 no.1
• /
• pp.13-19
• /
• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.1D
• /
• pp.71-77
• /
• 2010

This study presented the operation method through of productivity on eight analysis work items (TBM boring, cutter check and exchange, TBM maintenance, succeeding facilities, reinforcement in tunnel, operation alternation, a tram car) which have developed equipment at WRITH with TBM a waterway tunnel works. It was inquired lose time with analyzed result by work items and removed lose time. It was analyzed TBM boring length, TBM boring length percentage and TBM boring length time. This study analyzed TBM operation utility factor of a foreign work with TBM operation boring length percentage, a monthly average boring length, pure boring length percentage etc. and assumed a monthly average boring length and a monthly average boring length of rise forecast. Based on analyzed Data, TBM boring has been forecasted propriety pure boring length at compressive strength $675{\sim}1662kgf/cm^2$.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.17 no.30
• /
• pp.73-79
• /
• 1994

This paper propose Adaptive Maintenance as a new type of maintenance for machine failures which are unpredictable. A purpose of adpative maintenance is to decrease inconsistency. In order to pick up some of problems the traditional maintenance policy, We discussed Time Based Maintenance(TBM) and Condition Based Maintenance(CBM) with Bath-Tub Curve. By using Machine Condition Diagnosis Technique (CDT), Monitored condition maintenance deals with the dynamic decision making for diagnosis procedures at maintenance and caution level. Adaptive Maintenance is a powerful tool for Total Production Maintenance(TPM).

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.168-175
• /
• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Journal of the Society of Disaster Information
• /
• v.8 no.4
• /
• pp.328-335
• /
• 2012

The resource-based estimating based on standard unit price of construction work was estimated by multiplying the price per standard unit of work on the amount of labor, material, equipment use time. However, limitation of the resource-based estimating way does not adequately reflect the actual transactions prices. On the subject of water tunnel excavation as a new attempt to overcome these limitations, this study analyzed productivity by work type into cutter inspection/ exchange, TBM maintenance, TBM inspection/refueling, subsequent installations, tramcar, operating change, a cave-underground reinforcement / rock reinforcement, safety / meetings and analyzed actual cost estimating and the net advance rate based on this analysis result. Actual cost estimating calculation approach presented in this study can be utilized as a useful tool to predict the actual cost estimating in the TBM water tunnels field.

• Proceedings of the KSR Conference
• /
• 2011.05a
• /
• pp.1612-1617
• /
• 2011

Rolling stock needs many maintenance works because of its long service life. The maintenance of rolling stock has periodic preventive maintenance system. This periodic preventive maintenance system can't reflect the characteristics of every part. The condition-based maintenance system which reflects the functional condition of every part prevents breakdown and reduces maintenance cost. This study will analyze the records of every part by unreasonable examples of time-based preventive maintenance and reliability management activities, and discuss the necessity of maintenance system reflected the results.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.53-59
• /
• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Proceedings of the KSR Conference
• /
• 2008.11b
• /
• pp.235-240
• /
• 2008

Since urban railway transit is one of the most essential transportation systems, its power facilities must ensure high reliability and safety. Currently, urban railway operating organizations perform TBM (Time Based Maintenance) on power facilities. However, in order to improve management efficiency and system safety, CBM (Condition Based Maintenance) is preferred. Among various power facilities, mold transformers has been chosen as the object of study since it is widely used for the purpose of minimizing volume and weight, and due to safety against fire. In this paper, various transformer failure cases due to electric, thermal, mechanical and environmental factors have been collected and analyzed. In addition, investigation on national and international condition based maintenance cases and the characteristics of sensors widely used for transformer monitoring has been performed to suggest the optimal condition based maintenance technique for urban railway systems.

• Proceedings of the Safety Management and Science Conference
• /
• 2010.04a
• /
• pp.573-585
• /
• 2010

최근 철도차량은 안전성과 신뢰성 향상을 위해 점차 복잡하게 설계 제작되고, 품질에 대한 기대와 요구수준이 점차 높아짐에 따라 운영기관에서는 과학적이고 체계적인 예방 정비를 통한 안전성과 가용성 향상을 위해 노력하고 있다. 이러한 목적을 달성하기 위하여 여러 방안들이 연구되고 있으며, 대표적으로 신뢰성 기반 유지보수(RCM; Reliability Centered Maintenance)가 철도분야에 지속적으로 도입되고 있는 추세이다. 본 연구에서는 새로운 예방정비 기술로 대두되고 있는 RCM의 기본이론에 대한 고찰과 RCM의 일반적 실시 절차를 소개하고, RCM의 국제규격인 IEC 60300-3-11, NAVAIR 00-25-403, MIL-STD-2173을 비교 분석하여 이를 바탕으로 철도차량에 RCM 도입 시 효과적이고 적합한 절차 및 방안을 제시하고자 한다.