• Aerospace Engineering and Technology
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• v.6 no.2
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• pp.150-156
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• 2007

In this paper, we focus on the fault detection and isolation(FDI) method for inertial navigation system with three two-degree of freedom dynamically tuned gyros(DTG) on orthogonal sensor configuration. we propose the FDI method which can detect the fault of each DTGs rather than the fault of each sensing axis. The proposed FDI method is separated into two FDI modules according to the fault magnitude in order to improve the reliability of fault detection information. For large fault detection, only instantaneous DTG measurement is used to detect a fault DTG within a short time. For small fault detection, the integrated value of DTG measurements are used to detect a fault DTG. It takes a more time to detect a fault but it serves more reliable fault detection information. Using the proposed FDI method with consideration of DTG fault characteristic, we could find out a fault DTG successfully.

• Transactions of the KSME C: Technology and Education
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• v.3 no.3
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• pp.175-181
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• 2015

Because internal space of combat vehicle reachs about $80^{\circ}C$ at high temperature period, Proximity Sensor exposed high temperature and humidity, which has function to sense the distance and transfer signal for control unit, have enlarged sensing distance and finally locked on. Malfunction of sensing itself occur frequently, therefore we carried out cause analysis and improvement. We accomplish improvement activity secondly. Through-out many trial and error, we find out that malfunction of sensor occur at high temperature circumstance. To improve, the another Emitter Coil is added to increase voltage difference and improve sensing accuracy about 5~10 times. And we accomplish design improvement to dull temperature and humity change after increasing molding surface to add vibration and shock resistance. We prove that the improved product do not fail after enduring 136hr at $85^{\circ}C$ temperature and 85% relative humidity circumstance chamber.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1133-1141
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• 2009

In this paper, we present a distributed fault-tolerant topology control protocol that configure a wireless sensor network to achieve k-connectivity and (k+1)-coverage. One fundamental issue in sensor networks is to maintain both sensing coverage and network connectivity in order to support different applications and environments, while some least active nodes are on duty. Topology control algorithms have been proposed to maintain network connectivity while improving energy efficiency and increasing network capacity. However, by reducing the number of links in the network, topology control algorithms actually decrease the degree of routing redundancy. Although the protocols for resolving such a problem while maintaining sensing coverage were proposed, they requires accurate location information to check the coverage, and most of active sensors in the constructed topology maintain 2k-connectivity when they keep k-coverage. We propose the fault-tolerant topology control protocol that is based on the theorem that k-connectivity implies (k+1)-coverage when the sensing range is at two times the transmission range. The proposed distributed algorithm does not need accurate location information, the complexity is O(1). We demonstrate the capability of the proposed protocol to provide guaranteed connectivity and coverage, through both geometric analysis and extensive simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.301-301
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• 2022

본 연구는 운영 조건이 달라짐에 따라 회전수가 변하는 기기의 정상적 가동 여부와 고장 종류를 판별하기 위한 인공지능 알고리즘의 적용을 다루고 있다. 회전수가 변하는 장비로부터 계측된 상태 모니터링 센서의 신호는 비정상(non-stationary)적 특성이 있으므로, 상태 신호의 한계치가 고장 판별의 기준이 되기 어렵다는 점을 해결하고자 하였다. 정상 가동 여부는 이상 감지에 효율적인 오토인코더 및 기계학습 알고리즘을 적용하였으며, 고장 종류 판별에는 기계학습법과 합성곱 기반의 심층학습 방법을 적용하였다. 변하는 회전수와 연계된 주파수의 비정상적 시계열도 적절한 고장 특징 (Feature)로 대변될 수 있도록 시간 및 주파수 영역에서 특징 벡터를 구성할 수 있음을 예제로 설명하였다. 차원 축소 및 카이 제곱 기법을 적용하여 최적의 특징 벡터를 추출하여 기계학습의 분류 알고리즘이 비정상적 회전 신호를 가진 장비의 고장 예측에 활용될 수 있음을 보였다. 이 과정에서 k-NN(k-Nearest Neighbor), SVM(Support Vector Machine), Random Forest의 기계학습 알고리즘을 적용하였다. 또한 시계열 기반의 오토인코더 및 CNN (Convolution Neural Network) 적용하여 이상 감지와 고장진단을 수행한 결과를 비교하여 제시하였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.7
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• pp.493-502
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• 2001

This paper propose a new built-in current sensor(BICS) for current testing that has some advantages compared with conventional logic testing. The designed BICS detects the fault in circuit under test (CUT) and makes a Pass/Fail signal by comparison between CUT current and duplicated inverter current. The proposed circuit consists of a differential amplifier, a comparator and a inverter. It requires 10 MOSFETs and 3 inverters. Since the designed BICS do not require the extra clock, the added extra pin is only one output pin. The mode selection is not used in this circuit. Therefore we can apply the circuit to on-line testing. The validity and effectiveness are verified through the HSPICE simulation of circuits with defects. When CUT is a 8$\times$8 parallel multiplier, area overhead of the BICS is about 4.34%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.492-498
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• 2016

It is well known that since abrupt faults in induction motors tend to lead to subsequent faults and deterioration of the drive apparatus, motor faults may lead to several operating restrictions, such as security problems and economic loss. A lot of research has been done in the area of diagnosis to detect machine faults and to prevent catastrophic hazards in the motor drive system. This paper presents a new method of motor current signature analysis in which the DC-link current of the inverter-driven induction motor system, where a single current sensor is employed instead of three AC current sensors, is measured, and fast Fourier transform analysis is performed. This proposed method makes it possible to easily discern and clearly separate the motor fault current signature from the normal operation current flowing through the stator and rotor windings.

• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.671-678
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• 2009

Recently, there have been some researches in the equipment fault detection based on shock and vibration information. Most research of them is based on shock and vibration monitoring to determine the equipment fault or not. Different with engine fault detection based on shock and vibration information we focus on detection of engine for boat and system control. First, it use the duplicated-checking method for shock and vibration information to determine the engine fault or not. If there is a fault happened, we use the integral to determine the error engine shock wave width and detect the fault area. On the other hand, we use the engine trend analysis and standard of safety engine to implement the shock and vibration information database. Our simulation results show that the probability of engine fault determination is 98% and the probability of engine fault detection is 72%

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.706-712
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• 2009

In this paper, a fault diagnosis method is proposed to detect and classifies faults that occur in press process line. An oil level automatic monitoring method is also presented to detect oil level. The FFT(fast fourier transform) frequency analysis and ART2 NN(adaptive resonance theory 2 neural network) with uneven vigilance parameters are used to achieve fault diagnosis in proposing method, and GUI(graphical user interface) program for fault diagnosis and oil level automatic monitoring using LabVIEW is produced and fault diagnosis was done. The experiment results demonstrate the effectiveness of the proposed fault diagnosis method of induction motors and oil level automatic monitor system.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.282-283
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• 2020

3상 컨버터에서 스위치의 개방고장이 발생한 경우 고장 전류에 직류 및 고조파 성분이 발생할 수 있으며, 보호회로에 의한 고장 감지가 어려우므로 주변 기기에 2차 고장이 발생할 수 있다. 단일 및 이중 스위치 개방고장의 경우 21가지 고장 모드가 존재한다. 본 논문에서는 이러한 고장 모드를 진단하기 위해 정지 좌표계 d-q축 전류의 직류 및 고조파 성분을 활용하는 two-step 구조의 ANN(Artificial Neural Network)을 제안한다. 고장 시에 발생된 직류 및 고조파 성분 전류는 ADALINE(Adaptive-Linear Neuron)을 통해 얻는다. 고장 진단의 첫 번째 단계에서는 직류 성분을 기반으로 ANN을 이용하여 고장모드를 6개 영역으로 분류한다. 두 번째 단계에서는 6개의 각 영역에서 직류 성분과 전류의 THD(Total Harmonics Distortion)를 기반으로 ANN을 이용하여 개방고장이 발생한 스위치를 진단한다. 제안된 Two-step 방법으로 고장을 진단하므로써 간단한 구조로 ANN의 설계가 가능하다. 3.7kW급 3상 PWM 컨버터로 실험을 통해 제안된 방법의 효용성을 검증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.293-294
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• 2012

실시간으로 고장모듈을 교체하기 어려운 대다수의 군용 시스템에서 고장상황시 고장 모듈을 대체하기 위하여 동일 모듈의 중복운용 방식은 대다수의 시스템에서 사용되고 있다. 본 논문에서는 서비스 단위로 중복운용되는 무인체계 다계층 서비스 미들웨어에서의 고장복구 방법을 제안하고자 한다. 운용 상태 설정 정보에 따라 정상운용되는 미들웨어 시스템은 고장발생시 고장감지 흐름에 의해 고장을 판단하고, 고장복구 흐름에 따라 고장상황을 복구한다. 이를 통하여 이기종의 분산환경 상에서 예기치못한 시스템의 오동작이나 비가용 시에도 서비스 단위의 고장복구를 통한 가용성 증대 및 시스템 전체의 고신뢰성 목표수준을 충족시킬 수 있다.