• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.128-139
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• 2017

Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.11
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• pp.1315-1321
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• 2013

This study presents a prognostic technique for the damage state of a ball bearing. A stochastic bearing fatigue defect-propagation model is applied to estimate the damage progression rate. The damage state and the time to failure are computed by using RMS data from noisy acceleration signals. The parameters of the stochastic defect-propagation model are identified by conducting a series of run-to-failure tests for ball bearings. A regularized particle filter is applied to predict the damage progression rate and update the degradation state based on the acceleration RMS data. The future damage state is predicted based on the most recently measured data and the previously predicted damage state. The developed method was validated by comparing the prognostic results and the test data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.625-629
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• 2015

In this study, we develop a prognostic method of assessing the stator windings of power generators against water absorption through statistical data analysis and degradation modeling. The 42 windings of the generator are divided into two groups: the absorption and normal groups. A degradation model of a winding is constructed using Fick's second law to predict the level of absorption. By analyzing data from the normal group, we can determine the distribution of the data of normal windings. The health index of a winding is estimated using the directional Mahalanobis distance (DMD) method. Finally, the probability distributions of the failure time of the windings are determined.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1403-1412
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• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.11-12
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• 2019

본 논문에서 에스컬레이터 기계실 내부 전동기, 감속기, 구동 체인의 IoT 소음 및 진동 센서를 부착하여 에스컬레이터 운영중 실시간 상태 감시가 가능한 IoT 기반 에스컬레이터 고장 예지 시스템을 제안한다. IoT 소음 및 진동 센서는 에스컬레이터 운영 중 발생하는 소음 및 진동 데이틀 수집하여 PHM(Prognostics and Health Management) 서버로 전송하며, 서버에서는 진단 알고리즘을 통해 고장 유 무를 판단한다. 소음 데이터를 이용한 체인 피치 길이 알고리즘을 검증하기 위하여 실제 체인의 길이를 측정한 결과 값과 비교한 결과 99.8% 정확도를 가지며, 진동 데이터를 이용하여 전동기, 감속기의 상태 판단을 위한 알고리즘 검증을 위해 AST 사의 진동 센서와 비교한 결과 약간의 오차는 발생하지만 ISO 10816-3을 기준으로 한 판단 결과 값은 동일한 결과 값을 가지는 것을 확인하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2041_2042
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• 2009

The diagnostic/prognostic problems for condition based maintenance or Prognostics and Health Management has been used. Primary objectives of diagnosis/prognosis are maximizing system availability and minimizing downtime from fault isolation through more effective troubleshooting efforts. Diagnosis aims to detect the onset of failures to improve system performance and reduce life cycle cost by reducing the failure time. The prognosis can reduce operational and support total ownership cost and improve safety of machinery and complex systems. In this Paper, a fault diagnosis methodology has been described using a monopropellant propulsion system model as a test bench.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.8
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• pp.721-724
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• 2016

Because of the inherent uncertainties caused by the manufacturing process variations, future loading conditions, and incomplete damage models, the lifetimes of mechanical structures under field conditions are significantly different from the results obtained in the laboratories. In this study, a dual sensor was developed to prognosticate the fatigue failure of structures under these uncertain conditions, and its effectiveness was demonstrated on a rectangular columnar structure under repeated uni-axial loading. The dual sensor is a slightly weaker structure embedded in the target structure, so that failure occurs in the sensor earlier than in the target structure. From the signal differences in the strain gauges in the embedded dual sensor, it is possible to differentiate between the normal status and warning status, even under variable loads.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of Applied Reliability
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• v.15 no.4
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• pp.262-269
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• 2015

Accelerated degradation mechanism of the heat-resistant steel for high temperature plant was analysed in terms of microstructure and hardness. In order to simulate the microstructure of the steel actually used at $540^{\circ}C$ in the field, isothermal exposure was carried out at $630^{\circ}C$ up to 4,800 hours. The artificial degradation mechanism was comparatively verified to successfully simulate degradation of the long-time used field material. For the artificially degraded specimens, databases including size and aspect ratio of carbide, chemical composition (i.e., Cr/Mo ratio) of grain boundary carbide were built up. These degradation parameters were suggested as fingerprints for PHM (i.e., prognostics health management) of power plants.