• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.60-66
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• 2021

The importance of maintenance is leading the application of the maintenance strategy in various industries. The maritime industry is also a part of them, with changes in selecting and applying the maintenance strategy, but rather slowly, by retaining the old strategy. In particular, the ship is maintaining a previously used strategy. In the circumstance of the sea, ship requires a new suggestion for maintenance strategy. A ship predictive maintenance model predicts the breakdown or malfunction of machineries to secure maintenance time with preventive actions and treatments, thereby avoiding maintenance-related dangerous factors. This study focused on applying text analysis to an Liquefied Natural Gas Carriers dock indent document, and the analysis results were interpreted from the original document. The inter-relational patterns observed from the frequency of common maintenance combinations among different parts and equipment in ships will be applied to the development of ship predictive maintenance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.172-174
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• 2021

In the existing industry, maintenance was carried out in the form of preventive maintenance such as occurrence of unnecessary idle time due to limited monitoring and maintenance. However, with the advent of the Fourth Industrial Revolution, real-time monitoring is possible in many industries including mining, manufacturing, oil and gas, and commercial agriculture, and it is desired to minimize idle time due to maintenance. In particular, there is a growing interest in predictive maintenance that can reduce costs and maximize operational efficiency by predicting and maintaining a failure before equipment and equipment fail. In this study, we look at the predictive maintenance technology that can verify the abnormal condition of the equipment of the smart factory in advance and monitor the abnormal condition in real time.

• Journal of IKEEE
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• v.26 no.3
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• pp.488-493
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• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.10a
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• pp.138-143
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• 1994

기기의 결함을 진단하는데에 전문자동진단시스템(EADS)을 사용하는 것은 고도의 숙련된 진단요원 없이, 시스템저자와의 질의응답과 같은 일련의 회의를 갖지 않고도 정확하고 또한 믿을만하게 기기상태를 측정 분석할 수 있는 가장 효과적인 방법이다. 전문자동진단시스템(EADS)은 일분에 5개의 기기들을 분석하고 진동전문분석가에 버금가는(94%) 정확성으로 진단결과를 제공한다. 많은 전문진단시스템 중에서 DLI의 ExpertALERT[4]는 가장 정확하고 정교한 진단시스템으로 평가되고 있다. 전문자동진단시스템(EADS)의 시행으로 프렌트의 기기고장으로 인한 조업중단의 회수가 줄어지고 정비비용을 절감하며 불필요한 정기점검식정비(PM)을 없앤다면 관계기술요원들의 진동에 대한 이해와 기술습득으로 한차원 높은 기기 정비를 통해 효율적인 생산성증가, 정비비용감소[5], 안전사고 미연방지등 많은 것을 함께 얻을 수 있다. Expert System 기술의 성공적인 적용이라고 정의할 수 있겠다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.199-205
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• 2011

This paper proposes a framework to predict the mechanical fault of hot rolling mill system (HRMS). The optimum process of HRMS is usually identified by the rotating velocity of working roll. Therefore, observing the velocity of working roll is relevant to early know the HRMS condition. In this paper, we propose the framework which consists of two methods namely spectrum matrix which related to case-based fast Fourier transform(FFT) analysis, and three dimensional condition monitoring based on novel visualization. Validation of the proposed method has been conducted using vibration data acquired from HRMS by accelerometer sensors. The acquired data was also tested by developed software referred as hot rolling mill facility analysis module. The result is plausible and promising, and the developed software will be enhanced to be capable in prediction of remaining useful life of HRMS.

• Journal of KSNVE
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• v.4 no.4
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• pp.459-467
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• 1994

Various maintenance programs and techniques have been implemented for roating machineries, since machines were invented for commerical use. The earliest type of maintenance was run-to-failure, where the machine was run until a fault caused to fail in service. It was obviously an expensive approach due to the unpredictability of the machine condition. Another type is the periodic maintenance, where machines are disassembled and overhauled on regular schedules. With the advent of reliable data collectors including FFT analyzer and developing of versatile supporting software such as ExpertALERT system, the predictive maintenance is known to be the most feasible maintenance type these days. The vibration analysis enables for a maintenance crew to find the exact cause of fault on a machine and to make a proper maintenance schedule with a trend analysis. The predicitive maintenance is considered to be the most important part of pro-active maintenance.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.43-50
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• 2023

With the advent of industrialization, consumers and end-users demand more reliable products. Meeting these demands requires a comprehensive approach, involving tasks such as market information collection, planning, reliable raw material procurement, accurate reliability design, and prediction, including various reliability tests. Moreover, this encompasses aspects like reliability management during manufacturing, operational maintenance, and systematic failure information collection, interpretation, and feedback. Improving product reliability requires prioritizing it from the initial development stage. Failure mode and effect analysis (FMEA) is a widely used method to increase product reliability. In this study, we reanalyzed using the FMEA method and proposed an improved method. Domestic railways lack an accurate measurement method or system for maintenance, so maintenance decisions rely on the opinions of experienced personnel, based on their experience with past faults. However, the current selection method is flawed as it relies on human experience and memory capacity, which are limited and ineffective. Therefore, in this study, we further specify qualitative contents to systematically accumulate failure modes based on the Failure Modes Table and create a standardized form based on the Master FMEA form to newly systematize it.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.294-303
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• 2024

The objective of condition-based maintenance plus(CBM+) is to improve the availability and maintenance efficiency of missiles, bolstering national defense capabilities. This study proposes an application of CBM+ to enhance the reliability and the safety of missiles, which are the devices typically stored for long durations. CBM+ CBM+ does not only contribute to defense capabilities, but it also aims to reduce maintenance costs. This study focuses particularly on the dormant stage of the missile life-cycle, in which various failure modes and environmental impacts on failure mechanisms are investigated. The effectiveness of maintenance strategies and the implementation of CBM+ is evaluated using simulation data.