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軌道檢查系統技術研析-國內自主發展軌道檢查車可能性之探討

TRACK INSPECTION SYSTEM TECHNOLOGY - EXPLORATION OF THE POSSIBILITY OF DOMESTIC INDEPENDENT DEVELOPMENT OF TRACK INSPECTION VEHICLES

摘要


隨著臺灣鐵路運行里程逐年增長,傳統軌道巡檢方式與既有軌道檢查設備將難以滿足未來需求,近年來資通訊技術快速發展,工業4.0技術(Industry 4.0 technologies)已逐步應用於鐵路行業,軌道檢查系統的全面普及勢必為未來趨勢。本文探討軌道檢查系統的技術應用項目,主要區分為軌道結構狀態與幾何狀態之檢查。其中對於軌道幾何狀態的檢查技術,以國內自主發展軌道檢查車技術的可能性進行探討。本文試以國際目前軌道檢查系統的主流技術,結合光學測距與慣性定位技術建置軌道幾何檢查系統雛型,依據試驗結果初步推論國內具備自主發展之技術,並探討後續發展方向。

並列摘要


Based on the increasing operational mileage of Taiwan's railways, traditional track inspection methods and existing track inspection equipment will be difficult to meet future demands. With the development of information and communication technology, Industry 4.0 technologies have gradually been applied in the railway industry, and the widespread adoption of track inspection systems is inevitable for future trends. This article explores the technical application projects of track monitoring systems, mainly divided into checking track structure conditions and track alignment conditions. Among them, the possibility of domestically developing track inspection vehicle technology is discussed for the measuring of track geometry. Through this study, a prototype track inspection system is established by combining mainstream technologies of current international track inspection systems with optical ranging and inertial positioning technologies. Based on the experimental results, it is preliminarily inferred that Taiwan has the capability for independent development of technology and suggests relevant development directions and methods.

參考文獻


Heirich, O.,Robertson, P.,Garcia, A. C.,Strang, T.(2012).Bayesian train localization method extended by 3D geometric railway track observations from inertial sensors.15th International Conference on Information Fusion.(15th International Conference on Information Fusion).
Sharma, S.,Cui, Y.,He, Q.,Mohammadi, R.,Li, Z.(2018).Data-driven optimization of railway maintenance for track geometry.Transportation Research Part C: Emerging Technologies.90,34-58.
Tsunashima, H.(2019).Condition monitoring of railway tracks from car-body vibration using a machine learning technique.Applied Sciences.9(13)
Ngamkhanong, C.,Kaewunruen, S.,Costa, B. J. A.(2018).State-of-the-art review of railway track resilience monitoring.Infrastructure.3(1)
公益財団法人鉄道総合技術研究所,慣性正矢法による軌道検測,https://www.rtri.or.jp/rd/division/rd45/rd4530/rd45300106.html

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