• Smart Structures and Systems
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• 제29권4호
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 추계학술대회 특별세미나 특별세션
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• pp.189-197
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• 2006

Maglev using EMS becomes unstable by unexpected big air-gap disturbance. The main causes of the unexpected air-gap disturbance are step-wise rail joint and large distance between rail splices. For the stable operation of the Maglev, the conventional system uses the threshold method, which selects one gap sensor among two gap sensors installed on the magnet to read the gap between magnet and guide rail. But the threshold method with a wide bandwidth makes the discontinuous air-gap signal at the rail joints because of the offset in air gap sensors and/or the step-wise rail joins. Further more, in the case of the one with a narrow bend-width, it makes Maglev system unstable because of frequent alternation. In this paper, a new method using fuzzy rule to reduce air-gap disturbances proposed to improve the stability of Maglev system. It treats the air-gap signal from dual gap sensors effectively to make continuous signal without air gap disturbance. Simulation and experiment results proved that the proposed scheme was effective to reduce air-gap disturbance from dual gap sensors in rail joints.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1071-1072
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• 2006

Maglev using EMS becomes unstable by unexpected big air-gap disturbance. The main causes of the unexpected air-gap disturbance are step-wise rail joint and large distance between rail splices. For the stable operation of the Maglev, the conventional system uses the threshold method, which selects one gap sensor among two gap sensors installed on the magnet to read the gap between magnet and guide rail. But the threshold method with a wide bandwidth makes the discontinuous air-gap signal at the rail joints because of the offset in air gap sensors and/or the step-wise rail joins. Further more, in the case of the one with a narrow bend-width, it makes Maglev system unstable because of frequent alternation. In this paper, a new method using fuzzy rule to reduce air-gap disturbances proposed to improve the stability of Maglev system. It treats the air-gap signal from dual gap sensors effectively to make continuous signal without air gap disturbance. Simulation and experiment results proved that the proposed scheme was effective to reduce air-gap disturbance from dual gap sensors in rail joints.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.622-624
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• 2014

A maglev train records a host of physical variables such as gaps, voltages and currents for suspension control and monitoring purposes. These data available from a maglev contains wealth of information that can be explored for various uses. One possible of such application is to use the gap data to estimate the shape of the rail, especially at the joints where rails are connected. The eddy current sensors that measure the gap between the rail and the car body produce large peaks around the joints. The suspension controller discards these peaks. Since the shape of the peaks is related to the joint, however, these peaks can be utilized to estimate the shape of the joints. In this paper, we present preliminary results on estimating the joint shape using the peak data. The results show that the approach is promising, albeit several technical difficulties to overcome.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.310-312
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• 2006

Maglev using EMS becomes unstable by unexpected big air-gap disturbance. The main causes of the unexpected air-gap disturbance are step-wise rail joint and large distance between rail splices. For the stable operation of the Maglev, the conventional system uses the threshold method, which selects one gap sensor among two gap sensors installed on the magnet to read the gap between magnet and guide rail. But the threshold method with a wide bandwidth makes the discontinuous air-gap signal at the rail joints because of the offset in air gap sensors and/or the step-wise rail joins. Further more, in the case of the one with a narrow bend-width, it makes Maglev system unstable because of frequent alternation. In this paper, a new method using fuzzy rule to reduce air-gap disturbances proposed to improve the stability of Maglev system. It treats the air-gap signal from dual gap sensors effectively to make continuous signal without air gap disturbance. Simulation and experiment results proved that the proposed scheme was effective to reduce air-gap disturbance from dual gap sensors in rail joints.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1189-1194
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• 2011

도시형 자기부상열차는 전자기력으로 레일에서 낮은 높이로 부상하여 운행되는 열차이다. 자기부상열차는 궤도와의 직접적인 접촉 없이 움직이기 때문에 소음이 적고 기계적 마찰에 의한 진동이 거의 발생하지 않는다. 또한 급곡선 주행이 가능하고 경사도가 높은 언덕지역도 쉽게 주행 할 수 있기 때문에 도심에서의 새로운 교통수단으로 주목받고 있다. 따라서 도시형 자기부상열차의 성공적인 실용화를 위하여 제3궤조 전차선로 설비와 인터페이스를 통하여 안전성과 신뢰성 확보는 필수 고려 사항중 하나이다. 특히 파워레일, 신축확장장치, 절연 구분장치, 지지금구와 같은 제3궤조 전차선로 설비는 내 부식성, 시공의 편의성 및 유지보수성을 고려하여 설치되어야 한다. 본 논문에서는 자기부상열차 시스템 구축을 위한 제3궤조 설비의 구성요소에 대한 인터페이스 항목을 분석하고 전차선로의 안전성을 높일 수 있는 시스템 엔지니어링 인터페이스 체계를 제시하고자 한다. 또한 제안하는 자기부상용 제3궤조 전차선로 시스템은 한국기계연구원내 시험을 통하여 시스템의 안전성을 증명하였으며, 시스템 구성품의 수명분석을 통해 제3궤조 전차선로 운영의 신뢰성을 향상시키고자 한다.