• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• 한국통신학회논문지
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• 제39C권8호
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• pp.731-737
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• 2014

본 논문에서는 감시 카메라 환경에서 발생할 수 있는 군중의 비정상 행동 검출 방법을 제안한다. 군중들의 비정상 행동을 산발적으로 퍼지면서 뛰는 행동, 한쪽 방향으로 갑자기 뛰는 행동 두 가지로 정의하였다. 이를 검출하기 위하여 영상에서 움직임 벡터를 추출하여 군중의 비정상 행동 검출에 적합한 서술자 MHOF(Multi-scale Histogram of Optical Flow)와 DCHOF(Directional Change Histogram of Optical Flow)제안하였으며, 이를 이진 분류기인 SVM(Support Vector Machine)을 이용하여 검출하였다. 제안한 방법은 공개 데이터셋인 UMN 데이터와 PETS 2009 데이터를 이용하여 성능을 평가하였고 다른 방법론과의 비교를 통해 제안하는 알고리즘의 우수성을 입증하였다.

• 한국지능시스템학회논문지
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• 제22권4호
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• pp.405-413
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• 2012

심전도 신호의 신뢰성 있는 진단을 위해서는 높은 분류 정확도와 함께 낮은 오분류 성능이 중요하며, 특히 비정상을 정상으로 진단하는 것은 심검자에게 치명적인 문제로 귀결될 수 있다. 본 논문에서는 임상 진단 기준을 반영하는 규칙기반 분류 알고리즘을 이용하여 비정상 리듬을 검출 및 분류하는 방법을 제안한다. 규칙기반 분류는 리듬 구간의 특징에 대한 규칙 베이스를 이용하여 리듬 유형을 분류하도록 하며, 이 때 규칙 베이스는 임상 및 내과 분야의 심전도 전문 임상 자료에 기반한 본 논문의 기준표에 따라 구성된다. MIT-BIH 부정맥 데이터베이스를 이용한 제안 방법의 실험을 통하여 정상동조율, 박동조율, 및 다양한 비정상 리듬에 대한 리듬 유형의 분류가 가능함을 확인하였으며, 특히 비정상 리듬 검출 측면에서는 오분류가 전혀 발생되지 않는 결과를 보였다.

• 한국공작기계학회논문집
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• 제12권3호
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• pp.8-16
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• 2003

Monitoring of the drill wear :md hole quality change is conducted during the drilling process. Cutting force measured by tool dynamometer is a evident feature estimating abnormal state of drilling. One major difficulty in using tool dynamometer is that the work-piece must be mounted on the dynamometer, and thus the machining process is disturbed and discontinuous. Acoustic transducer do not disturb the normal machining process and provide a relatively easy way to monitor a machining process for industrial application. for this advantage, AE signal is used to estimate the abnormal fate. In this study vision system is used to detect flank wear tendency and hole quality, there are many formal factors in hole quality decision circularity, cylindricity, straightness, and so of but these are difficult to measure in on-line monitoring. The movement of hole center and increasement of hole diameter is presented to determine hole quality. As the results of this experiment AE RMS signal and measurements by vision system are shorn the similar tendency as abnormal state of drilling.

• 항공우주시스템공학회지
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• 제18권3호
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• pp.41-47
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• 2024

최근에 무인항공기의 실용화 및 사업화가 추진됨에 따라 무인항공기의 안전성 확보에 관한 관심이 증가하고 있다. 무인항공기의 사고는 재산 및 인명 피해를 발생시키기 때문에 사고를 예방할 수 있는 기술의 개발은 중요하다. 이러한 이유로 AutoEncoder 모델을 이용한 비정상 비행 상태 탐지 기법이 개발되었다. 그러나 기존 탐지 기법은 성능과 실시간 처리 측면에서 한계를 지닌다. 본 논문에서는 U-Net 기반 비정상 비행 탐지 기법을 제안한다. 제안하는 기법에서는 U-Net 모델에서 얻어지는 재구성 오차에 대한 마할라노비스 거리 증가량에 기반하여 비정상 비행이 탐지된다. 모의실험을 통해 제안 탐지 기법이 기존 탐지 기법에 비해 탐지 성능이 우수하며 온보드 환경에서 실시간으로 구동될 수 있음을 알 수 있다.

• Journal of Positioning, Navigation, and Timing
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• 제11권2호
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• pp.109-117
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• 2022

This paper proposes a method for detecting flight anomalies of drones through the difference between the command of flight controller (FC) and the navigation solution. If the drones make a flight normally, control errors generated by the difference between the desired control command of FC and the navigation solution should converge to zero. However, there is a risk of sudden change or divergence of control errors when the FC control feedback loop preset for the normal flight encounters interferences such as strong winds or navigation sensor abnormalities. In this paper, we propose the method with a deep neural network model that predicts the control error in the normal flight so that the abnormal flight state can be detected. The performance of proposed method was evaluated using the real-world flight data. The results showed that the method effectively detects anomalies in various situation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.452-455
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• 2005

The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• 한국정밀공학회지
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• 제26권11호
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Journal of Electrical Engineering and Technology
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• 제9권4호
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• pp.1171-1181
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• 2014

Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.