• Ecology and Resilient Infrastructure
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• 제6권2호
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• pp.120-127
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• 2019

본 연구에서는 광섬유 온도 분포 센싱을 통한 제체의 침투 및 붕괴와 같은 물리적 변화 현상을 모니터링하기 위해 중규모 제방 수리실험을 수행하였다. 본 실험의 중규모 실험 제방은 바이오폴리머 흙을 제방 전면에 도포하여 강도를 증진시킨 것으로 월류에 의한 침투 및 붕괴 현상이 일반 제방과는 다르게 나타날 수 있으며, 이러한 현상은 광섬유 온도 분포 센싱을 통해 획득한 온도 변화 정보를 통해 분석할 수 있었다. 제체의 위치별 시간에 따른 온도 변화 자료를 통해 제체 내부의 물리적 변화 및 침투가 발생하는 위치와 시간을 판단할 수 있었다. 본 실험에서는 급격한 온도 변화 시점이 제외지 사면보다 제내지 사면에서 먼저 발생하였으며, 이는 실험에서 제내지 사면이 붕괴된 후에 제외지 사면이 붕괴된 순서와 일치하였다.

• Journal of Computing Science and Engineering
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• 제8권1호
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• pp.17-24
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• 2014

In this paper, we propose a new implementation of a failure detector. The implementation uses a dual model of heartbeat and interaction. First, the heartbeat model is adopted to shorten the detection time, if the detection process does not receive the heartbeat message in the expected time. The interaction model is then used to check the process further. The expected time is calculated using the exponential smoothing method. Exponential smoothing can be used to estimate the next arrival time not only in the random data, but also in the data of linear trends. It is proven that the new detector in the paper can eventually be a perfect detector.

• 센서학회지
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• 제19권2호
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• pp.124-129
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• 2010

For the industrial printing applications, the stability of the piezo-driven inkjet printhead is a major requirement. In this paper, we focused on the failure modes of the inkjet printhead and realized a method to detect and repair them at high speed. The printhead monitoring is performed by detecting the residual vibration of the actuating plate using the self- sensing capability of the piezoelectric material. To measure the channel acoustics and to identify the malfunctioning nozzle, we devised the bridge sensing circuitry and failure detection algorithm. The residual vibration signals can be affected by the boundary conditions of the channel acoustics, so it is possible to identify the failure causes by analyzing the monitoring signals. Therefore it is also possible to apply a proper restoring process to the defective printhead. The experimental results show that this method is effective in improving the reliability of the industrial printing.

• Structural Engineering and Mechanics
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• 제69권4호
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• pp.407-416
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• 2019

This paper develops a wireless sensor for online fatigue crack detection and failure warning based on crack-induced nonlinear ultrasonic modulation. The wireless sensor consists of packaged piezoelectric (PZT) module, an excitation/sensing module, a data acquisition/processing module, a wireless communication module, and a power supply module. The packaged PZT and the excitation/sensing module generate ultrasonic waves on a structure and capture the response. Based on nonlinear ultrasonic modulation created by a crack, the data acquisition/processing module periodically performs fatigue crack diagnosis and provides failure warning if a component failure is imminent. The outcomes are transmitted to a base through the wireless communication module where two-levels duty cycling media access control (MAC) is implemented. The uniqueness of the paper lies in that 1) the proposed wireless sensor is developed specifically for online fatigue crack detection and failure warning, 2) failure warning as well as crack diagnosis are provided based on crack-induced nonlinear ultrasonic modulation, 3) event-driven operation of the sensor, considering rare extreme events such as earthquakes, is made possible with a power minimization strategy, and 4) the applicability of the wireless sensor to steel welded members is examined through field and laboratory tests. A fatigue crack on a steel welded specimen was successfully detected when the overall width of the crack was around $30{\mu}m$, and a failure warnings were provided when about 97.6% of the remaining useful fatigue lives were reached. Four wireless sensors were deployed on Yeongjong Grand Bridge in Souht Korea. The wireless sensor consumed 282.95 J for 3 weeks, and the processed results on the sensor were transmitted up to 20 m with over 90% success rate.

• 디지털산업정보학회논문지
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• 제14권3호
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• pp.29-38
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• 2018

It is the approach of embedded system design that analyzes COS(Cut Out Switch) failure in the power distribution and an instantaneous breakdown of power distribution supply could cause the weakness of industrial competence and therefore we need to feed the stable power distribution with developing the technology of open-source embedded system. In this paper, we apply the LoRa technology which is the Internet of Things(IoT) protocol for low data rate, low power, low cost and long range sensor applications. We designed the hardware and software architecture setup and experimented the embedded system with network architecture and COS monitoring system including accelerometer for detecting the failure of distribution line and sensing the failure of its fuse holder by recognizing the variation and collision and afterwards sending the information to a gateway. With experimenting we designed the embedded platform for sensing the variation and collision according to the COS failure, monitoring its fuse holder status and transferring the information of states with LoRa technology.

• Journal of Yeungnam Medical Science
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• 제11권1호
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• pp.181-185
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• 1994

저자들은 완전방실블록 환자에서 쌍극의 영구심장박동기를 이식후에도 계속되는 실신발작을 보여 단극의 영구심장박동기로 바꾼 후 그 증상이 소실된 예를 경험하였기에 이를 보고하는 바이다.

• Journal of Power Electronics
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• 제19권2호
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.757-758
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• 2009

• 한국정밀공학회지
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• 제13권4호
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• pp.136-142
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• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• Ocean Systems Engineering
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• 제7권2호
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.