• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1917-1918
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• 2006

아크 고장검출차단기(Air Fault Circuit Interrupter)는 전기배선에 있어서 노화, 절연파괴등 아크를 검출하여 전기화재를 미연에 방지해준다. 반면 여러가지 아크성 신호가 혼재되어 있어 유사 아크로 인해 유해 아크만을 검출하지 못하여 오동작하게 된다. 따라서 아크전류를 검출하기 위해서는 아크전류와 혼동되는 많은 신호들을 분석할 필요가 있으며, 이러한 전기 기구에서 발생하는 노이즈와 전기 도선에서 발생한 아크 전류를 분류하여 전기 도선에서 발생하는 유해 아크 전류만을 검출하여 차단하도록 설계하였다.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.209-218
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• 2014

An accident related with elevators can cause death or serious injury of operators or passengers. This kind of a fatal accident is due to a failure of elevator. The reduction of failures of elevators is important to reduce the occurrence of elevator accident. Thus, this paper presents the results of analysis for the failure of elevators using the failure data of elevator. The results of analysis can be used to make a maintenance process of elevators.

• Annual Conference of KIPS
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• 2016.10a
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• pp.401-403
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• 2016

최근 태양광 발전사업의 투자 수요가 증가하고 있으며, 이에 따른 태양광 발전시스템 (PV시스템)의 신뢰성 및 발전 효율 향상 등을 확보할 수 있는 모니터링 시스템의 중요성이 부각되고 있다. 본 논문에서는 데이터를 앙상블 기법으로 분석하여 알려진 자동 분류 기법과 앙상블 기법을 비교해보고, 이를 바탕으로 PV시스템 고장 예측의 정확도를 향상 시키고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.19-20
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• 2016

본 논문에서는 서포트 벡터 머신 (SVM)의 분류 성능에 영향을 주는 인수인 C와 ${\sigma}$ 값을 적응적으로 최적화할 수 있는 적응적 자가튜닝 SVM을 이용한 베어링의 상태 진단 방법을 제안한다. SVM의 각 인수의 변화에 따른 베어링 상태 진단의 성능 변화 패턴을 분석하여 적합한 인수를 적응적으로 찾을 수 있는 방법을 제안하고, 제안한 방법의 우수성을 검증하기 위해 실제 베어링 신호를 이용하여 기존방법인 격자탐색과의 성능을 비교하였다.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.808-810
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• 1996

In this paper, neural network, which has learning capability, is used for fault type classification and fault section estimation for high speed relaying. The potential of the neural network approach is demonstrated by simulation using ATP. The instantaneous values of voltages and currents are used the inputs of neural networks. This approach determines the fault section directly. In this paper, back-propagation network(BPN) is used for fault type classification and fault section estimation and can use for high speed relaying because it determines fault section within a few msec.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.13-14
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• 2020

본 논문에서는 엣지 컴퓨팅 환경에서 머신러닝을 활용해 에스컬레이터 이상 감지 및 결함 분류를 하는 연구를 진행하였다. 엣지 컴퓨팅 기반 머신러닝을 사용해 에스컬레이터의 이상 감지 및 결함 분류를 위한 OneM2M환경을 구축하였으며 에스컬레이터에서 발생하는 소음에서 고장 유형에 따라 나타나는 주파수를 이용한다. Edge TPU를 활용해 엣지 컴퓨팅 시스템의 처리량을 최대화하고, 각 작업의 수행시간을 최소화함으로써 엣지 컴퓨팅 환경에서 이상 감지와 결함 분류를 수행할 수 있다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.2
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• pp.47-58
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• 2009

In this study, a seismic fragility analysis was performed for substation systems in Korea. To evaluate the seismic fragility function of the substation systems, a fragility analysis of the individual equipment and facilities of the substation systems was first performed, and then all systems were considered in the fragility analysis of the substation systems using a fault-tree method. For this research, the status of the substation systems in Korea was investigated for the classification of the substation systems. Following the classification of the substation systems, target equipment was selected based on previous damage records in earthquake hazards. The substation systems were classified as 765kV, 345kV, and 154kV systems. Transformer and bushing were chosen as target equipment. The failure modes and criteria for transformer and bushing were decided, and fragility analysis performed. Finally, the fragility functions of substation system were evaluated using the fault tree method according to damage status.

• 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 the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.409-415
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• 2016

Recently several techniques for disaster prevention based on IoT(Internet of Things) are being developed. In this paper, a new smart pattern classification method for electric loads is proposed. CT(Current Transformer) data are extracted from electric loads, and then the sampled CT data are converted using FFT and MFCC. FFT and FMCC data are used for the input data of neural networks. Experiments were conducted using FFT and MFCC data for 7 kinds of electric loads. Experiments results indicate the superiority of MFCC in comparison to FFT.