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Smart elasto-magneto-electric (EME) sensors for stress monitoring of steel structures in railway infrastructures

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2011年第12期12卷 895-901页

作者： Yuan-feng DUAN Ru ZHANG Yang ZHAO Siu-wing OR Ke-qing FAN Zhi-feng TANG College of Civil Engineering and Architecture Zhejiang University Hangzhou 310058 China Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong China School of Information Engineering Wuyi University Jiangmen 529020 China

steel structures are widely used in railway *** stress state is the most important determinant of the safety of these *** elasto-magnetic (EM) sensor is the most promising for stress monitoring of in-service steel ***,the necessity of magnetic excitation to saturation due to the use of a secondary coil for signal detection,keeps from its engineering *** this paper,a smart elasto-magneto-electric (EME) sensor using magneto-electric (ME) sensing units to take the place of the secondary coil has been exploited for the first *** ME sensing unit is made of ME laminated composites,which has an ultrahigh ME voltage coefficient and can measure the magnetic induction simply and *** analysis and characterization experiments firstly conducted on the ME laminated composites showed that the ME sensing units can be applied in the EM sensor for improved performance in stress monitoring.A tension test of a steel bar was carried out to characterize our smart EME sensor and the results showed high accuracy and *** present smart EME sensor is a promising tool for stress monitoring of steel structures in railway and other civil infrastructures.

关键词： Stress monitoring High-speed railway steel rail Elasto-magnetic (EM) sensor Magneto-electric (ME) Elasto-magneto-electric (EME) sensor

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Application of improved back-propagation algorithms in classification and detection of scars defects on rails surfaces

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High Technology Letters 2018年第3期24卷 249-256页

作者：石甜 Kong Jianyi Wang Xingdong Liu Zhao School of Machinery and Automation Wuhan University of Science and Technology

An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be ***,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.

关键词： detection platform steel rail improved algorithm defect classification identification rate

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