• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.1
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• pp.1-7
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• 2008

Submarine cable is the most important IT infrastructure for international communication across oceans. However, a cable fault rarely happens by ship's anchor, fishing gears, submarine earthquake, and so on, and we need to improve on repair time for the reducing expenses of cable repair ship as well as the stability of high-capacity submarine optical network. There are several kinds of cable faults such as Shunt fault, Cable cut, Open fault and Fiber break. When a fault is occurred, cable landing stations(CLS) have to analysis failure quickly and accurately to find the type and the location of a cable fault. During the repair period, CLS should swiftly perform the tests requested by cable repair ship. In order to make rapid progress on cable repair, CLS test technique is very important. So, in order to reduce the repair time, this paper is studying the CLS test techniques of locating a submarine cable fault and of checking the splicing point performed by cable repair ship.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.224-226
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• 2022

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.54-56
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• 2021

다양한 소스로부터 수집되고 연동되는 데이터를 모델링하는 기술로 그래프 데이터베이스를 활용한 분석 기법이 각광받고 있다. 이 연구에서는 항로표지에서 관측되는 상태 및 주변 정보를 모델링하고, 고장진단 및 예측에 적용할 수 있는 기계학습 기법을 소개한다.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2076-2078
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• 2003

전력 케이블의 고장점 탐지를 상시 감시하고 있는 데이터로부터 on-line으로 할 수 있도록 하기 위한 전력 케이블 고장 신호의 전처리 기술 개발에 관하여 언급하였다. 고장 전류 파형을 모의하고 측정할 수 있는 케이블 고장 모의 측정 시스템을 구성하여 고장 전압과 전류 파형을 측정하였으며 측정된 신호로부터 고장점을 보다 정확하고 빠르게 연산할 수 있도륵 하기 위한 down sampling, filtering 등 전처리 과정에 대하여 시뮬레이션하고 그 결과에 대하여 논하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.67-74
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• 2023

Recently, Urban Aircraft Mobility (UAM) has been attracting attention as a transportation system of the future, and small drones also play a role in various industries. The failure of various types of aviation systems can lead to crashes, which can result in significant property damage or loss of life. In the defense industry, where aviation systems are widely used, the failure of aviation systems can lead to mission failure. Therefore, this study proposes an anomaly detection model using deep learning technology to detect anomalies in aviation systems to improve the reliability of development and production, and prevent accidents during operation. As training and evaluating data sets, current data from aviation systems in an extremely low-temperature environment was utilized, and a deep learning network was implemented using the convolutional neural network, which is a deep learning technique that is commonly used for image recognition. In an extremely low-temperature environment, various types of failure occurred in the system's internal sensors and components, and singular points in current data were observed. As a result of training and evaluating the model using current data in the case of system failure and normal, it was confirmed that the abnormality was detected with a recall of 98 % or more.

• Proceedings of the KIEE Conference
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• 2008.10a
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• pp.79-80
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• 2008

제주-해남간 운영중인 HVDC 해저케이블에서 발생한 절연파괴사고('06.4)와 관련하여 써징코일을 이용한 고장점 탐색을 실시하고 고장지점 추정 및 향후 대책마련에 활용하코자, 제주-해남 HVDC 해저케이블 고장점 모의용 EMPT 모델링을 개발하였으며 유인잠수정을 이용한 실측 데이터 값과 EMTP를 활용한 시뮬레이션값을 비교하였다. 향후, 다양한 HVDC 해저케이블 고장유형에 따른 과도해석용 시뮬레이션을 위하며 본 논문에서 개발한 EMTP 모델링 활용이 가능할 것으로 생각된다.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.