• Aerospace Engineering and Technology
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• v.12 no.2
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• pp.14-23
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• 2013

Fault management design of the satellite describes preparations for failures which can occur during operational phase. Fault management design contains detection and isolation function of anomaly, and also it contains function to maintain the satellite in safe condition until the ground station finds out a cause of failure and takes a countermeasure. Unlike normal operation, safing operation is automatically performed by Power Control and Distribution Unit and Integrated Bus Management Unit which loads Flight Software without intervention of ground station. Since fault management operation is automatical, fault management logic and functionality of relevant hardware should be thoroughly checked during ground test phase, and error which is similar to actual should be carefully applied without damage. Verification test for fault management design is conducted for various subsystems of satellite. In this paper, we show the design process of fault management design verification test for Electrical Power Subsystem and Attitude and Orbit Control Subsystem of Low Earth Orbit satellite flight model and the test results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.3
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• pp.81-86
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• 2006

This paper deals with a driving current fault monitor design methodology for a DDV actuation system which has a dual hydraulic power supply system, and triplex electric control capability. A fault existing among these redundant channels should be detected accurately and removed timely, and the remaining channels are to be reconfigured in order to compensate the role of a removed faulty channel. An integrated analysis on the aerodynamics, flight control laws, and DDV actuation system is essential for the design of an actuation system fault monitor. A method to define a fault transient boundary which specifies a maximum travel of an actuation system caused by the first faulty operation is proposed based on the top level requirement on the fault effect specified in MIL-F-8785C.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.186-191
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• 2004

전력계통에서 역률개선을 위해 사용하는 전력용 콘덴서(이하 SC bank)는 전력계통의 운전상 전압 제어와 역율개선이라는 두가지 측면에서 매우 중요한 전력설비이다. 또한 최근에 전력산업 구조조정에 의해 발전분야가 분할되는 시점에서 SC bank의 역할의 중요성이 강조되고 있다. 본 논문에서는 SC의 고장원인이 무엇인지를 현장조사를 통해 수행하였다. 그 결과 SC bank를 구성하고 있는 리액터, 콘덴서, PT, 혹은 CT 흑은 방전코일의 열화원인이 대부분 차단기의 투입서지와 차단서지에 의해 발생되고 있음을 확인하였다 또한 기존의 보호시스템은 SC Bank의 보호에 적합하지 않음을 검토하였다 차단기 서지에 대한 대책으로는 투입시 전압영점투입과 중성점 저항기의 취부로 투입서지 및 차단서지를 효과적으로 감소되는 현상을 모의와 현장실측을 통해 확인하였다. 또한 기존의 보호방식이 과전류와 과전압 부족전압 혹은 CT와 PT를 이용한 차동방식에 의해 셀의 경련변화를 감지하고 있으나 이 경우 보호맹점이 존재하게 됨을 검토하였다. 이러한 보호상의 문제점을 보완하는 방법으로 SC bank는 임피던스가 늘 일정하는 점에 착안하여 전압과 전류를 이용하여 임피던스의 변화량을 감시하고, 또 한가지 방법은 SC bank의 운전특성상 무효전력만을 발생시킨다는 점에 착안하여 만일 유효전력 성분이 SC bank에서 감지된다면 소자의 이상이나 비정상적은 전류경로가 된다는 결론에 도달하고 유효전력감시를 통한 SC bank의 열화감지가 가능하다는 결론에 도달하였다. 본 논문에서 제안한 3가지의 방법, 즉, 첫째 영점투입차단기 채택, 둘째, 중성점 저항기의 도입, 셋째 새로운 보호방식에 의한 기존의 보호맹점의 보완을 제안하였다. 위의 새로운 제안을 현장에 적용하는 경우 제작사의 제작불량을 제외한 운전상의 SC bank의 문제점 및 고장빈도는 현저히 감소하게 될 것으로 사료된다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Fire Science and Engineering
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• v.25 no.4
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• pp.48-55
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• 2011

This paper presents the failure analysis of the rate of rise spot type heat detectors on artificially accelerated aging. The failures of heat detector turned out by two reasons. The first one is the separation of binder from plastic moulding, resulting in the leakage of air from heat chamber. The second reason is the crack of plastic. The large cracks were maybe created by these reasons, thermal expansion difference, mechanical stress, or growth of microcrack. In the sound detector, the separation and the crack were not occurred or not developed to the critical size. The glass fibers which increase the mechanical strength were added in the binder of detector 2010G. The densities of binder or plastic of each detector were similar. However, the TGA result shows that the thermal characteristics of 2005A and 2005B were not similar.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.562-567
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• 2020

In this paper, this is a study on the correction of protection relays to monitor temporary power facilities for storage tanks especially transformers to block and protect faults such as insulation breakdown. When an abnormality such as a short circuit or a ground fault occurs in the power system, it is important to detect this quickly cut off the device and equipment in which the fault occurred and separate it from the power system to correct the protection relay so that it does not interfere with power supply. In addition the fault current calculation that accurately applies the fault type and the cause of the fault for protection cooperation will be the most important factor in the correction of the protection relay. For protection coordination a study was conducted on the method of coordination for protection of power facility protection for storage tanks such as over current relay, ground over current relay, under voltage relay, and ground over voltage relay applied to temporary.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. 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 the Korean Society for Aeronautical & Space Sciences
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• v.48 no.12
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• pp.1005-1012
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• 2020

Avionics systems that use the PCI-Express bus unfortunately cannot use at least one I/O device if the bus fails, because the I/O device is connected to CPU through only one PCI-Express channel. This paper presents a fault-tolerant architecture of the PCI-Express bus for avionics systems, which tolerates one channel failure with help of the other redundant channel that has not been failed. In this architecture, each redundant PCI-Express channel connects a corresponding port of CPU to each switch logic of channels to provide each I/O device through a switched fault-tolerant channel. This paper includes the results of experimentation to show that the architecture detects the faulty condition in real time and switches the channel to the other redundant channel which has not been failed, when the architecture meets a failure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.