The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Stephen R Sharp - One of the best experts on this subject based on the ideXlab platform.
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evaluation of stainless steel Fasteners for bolted field splice connections of astm a1010 corrosion resistant steel plate girders
Transportation Research Record, 2017Co-Authors: Thomas R Williams, Xuemeng Xia, Thomas E Darby, Stephen R SharpAbstract:The Virginia Department of Transportation, Richmond, initiated this study to compare the mechanical properties, availability, and costs of stainless steel fastener materials for use with ASTM A1010 stainless steel plate. The investigation focused on fastener materials included in ASTM A193 and compared them with ASTM A325 bolts. The ASTM A193 bolts tested were the B6, B8, and B8M. Test results indicated that the ASTM A193 B8 Fasteners provided the most economic combination of mechanical strength, corrosion resistance, and cost. Uniaxial tension tests and Skidmore–Wilhelm rotational capacity tests revealed that the B6 Fasteners had high strength but lower ductility, whereas the B8M Fasteners had lower strength but higher ductility. The B8 fastener had an ideal combination of strength and ductility. The mechanical performance of the ASTM A193 Fasteners was improved further by the use of hardened washers. Because the bolts are hot forged, sensitization, which can reduce corrosion resistance, was of concern. ...
Qingzhou Mao - One of the best experts on this subject based on the ideXlab platform.
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a rigorous fastener inspection approach for high speed railway from structured light sensors
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Qingzhou Mao, Qingwu Hu, Hao Cui, Xiaochun RenAbstract:Abstract Rail Fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose Fasteners. Firstly, precise and extremely dense point cloud of Fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the Fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal Fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
Xiaochun Ren - One of the best experts on this subject based on the ideXlab platform.
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a rigorous fastener inspection approach for high speed railway from structured light sensors
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Qingzhou Mao, Qingwu Hu, Hao Cui, Xiaochun RenAbstract:Abstract Rail Fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose Fasteners. Firstly, precise and extremely dense point cloud of Fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the Fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal Fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
Hui Zhao - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways
Sensors, 2011Co-Authors: Jin-feng Yang, Manhua Liu, Yong-jie Zhang, Wei Tao, Haibo Zhang, Hui ZhaoAbstract:Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect Fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of Fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of Fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h.
Qingwu Hu - One of the best experts on this subject based on the ideXlab platform.
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a rigorous fastener inspection approach for high speed railway from structured light sensors
Isprs Journal of Photogrammetry and Remote Sensing, 2017Co-Authors: Qingzhou Mao, Qingwu Hu, Hao Cui, Xiaochun RenAbstract:Abstract Rail Fasteners are critical components in high-speed railway. Therefore, they are inspected periodically to ensure the safety of high-speed trains. Manual inspection and two-dimensional visual inspection are the commonly used methods. However, both of them have drawbacks. In this paper, a rigorous high-speed railway fastener inspection approach from structured light sensors is proposed to detect damaged and loose Fasteners. Firstly, precise and extremely dense point cloud of Fasteners are obtained from commercial structured light sensors. With a decision tree classifier, the defects of the Fasteners are classified in detail. Furthermore, a normal vector based center extraction method for complex cylindrical surface is proposed to extract the centerline of the metal clip of normal Fasteners. Lastly, the looseness of the fastener is evaluated based on the extracted centerline of the metal clip. Experiments were conducted on high-speed railways to evaluate the accuracy, effectiveness, and the influence of the parameters of the proposed method. The overall precision of the decision tree classifier is over 99.8% and the root-mean-square error of looseness check is 0.15 mm, demonstrating a reliable and effective solution for high-speed railway fastener maintenance.
