• 제목/요약/키워드: track-side sensing system

검색결과 3건 처리시간 0.016초

Wheel tread defect detection for high-speed trains using FBG-based online monitoring techniques

  • Liu, Xiao-Zhou;Ni, Yi-Qing
    • Smart Structures and Systems
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    • 제21권5호
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    • pp.687-694
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    • 2018
  • The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

Occupancy 센서와 도플러 Radar를 이용한 침상 모니터링 시스템 (Bed Side Monitoring System using Occupancy Sensor and Doppler Radar)

  • 강병욱;유선국
    • 한국멀티미디어학회논문지
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    • 제21권3호
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    • pp.382-390
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    • 2018
  • A major accident occurring on the bed is falls that occur during at times when the care of nurses or protectors is inadequate, which is fatal to patients or the elderly. In particular, Enuresis or sleepiness caused by sleep apnea increases the risk of falls. Therefore, it is very important to detect falls and sleep apnea of patients without infringing privacy in the bed to patient's safety and accident prevention. In this paper, we reviewed the technologies developed for bed monitoring and implemented a non-intrusive monitoring system. The Occupancy Sensor allows the temperature of the bed and surrounding area to be extracted to enable track of the patient's motion. The Doppler Radar detects the patient's movements at normal times and the respiration state when patients have no movement during sleeping. It is specially designed for real-time monitoring of falling and respiration during sleeping through contactless multi-sensing while solving patient's privacy problems.

드론기반 시공간 초분광영상 및 RGB영상을 활용한 추적자 농도분석 기법 개발 (Development of tracer concentration analysis method using drone-based spatio-temporal hyperspectral image and RGB image)

  • 권영화;김동수;유호준;한은진;권시윤;김영도
    • 한국수자원학회논문집
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    • 제55권8호
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    • pp.623-634
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    • 2022
  • 하천 주변 친수구역 조성, 4대강 사업 등과 같은 하천정비 사업으로 인해 하천의 흐름특성은 계속적으로 변동하고 있으며, 각종 오염물질 유입으로 인한 수질사고의 위험이 높아지고 있다. 수질사고 발생시 하천의 흐름특성을 고려해 오염물질의 농도 및 도달시간을 예측해 신속한 방제작업으로 하류로의 영향을 최소화해야한다. 이러한 오염물질의 거동을 추적하기 위해서는 하천의 구간별 확산계수, 분산계수 산정이 필요하며 그중 분산계수는 용존성 오염물질의 확산범위 해석에 사용된다. 오염물질의 거동을 추적하기 위한 기존 실험적 연구사례들은 많은 인력과 비용이 소요되고, 한정적인 장비의 운용으로 공간적으로 높은 해상도의 자료 취득이 어려웠다. 최근에는 RGB드론을 이용한 오염물질의 추적연구가 수행되었지만, RGB영상 역시 분광정보를 한정적으로 수집한다는 한계가 있다. 본 연구에서는 기존 연구들의 한계점들을 보완하기 위해 드론을 활용한 원격탐사 플랫폼에 초분광센서를 탑재하여 기존 접촉식 측정보다 시간적, 공간적으로 고해상도의 자료를 수집하였다. 수집된 시공간(Spatio-temporal) 초분광영상을 활용해 추적자의 농도를 산정하고, 횡분산계수를 도출하였다. 향후 연구를 통해 드론 플랫폼의 한계를 극복하고, 분산계수 산정 기술을 고도화하면 수계로 유출되는 각종 오염물질의 감지 및 다양한 수질항목 및 하천인자의 변화량 감지가 가능할 것으로 기대된다.