Broken Wires Diagnosis Method Numerical Simulation Based on Smart Cable Structure  

作　　者：Sheng LI Min ZHOU Yan YANG 

机构地区：[1]National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan,430070, China [2]Wuhan WUTO.S Limited Company, Wuhan, 430223, China [3]Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, WuhanUniversity of Technology, Wuhan, 430070, China

出　　处：《Photonic Sensors》2014年第4期366-372,共7页光子传感器（英文版）

基　　金：The research work reported in this paper was supported by the National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, China. Thanks for the support of the Fundamental Research Funds for the Central Universities （WUT： 2014-IV-090） and the National Natural Science Foundation of China （Major Program： 61290310）. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author（s） and source are credited.

摘　　要：The smart cable with embedded distributed fiber optical Bragg grating （FBG） sensors was chosen as the object to study a new diagnosis method about broken wires of the bridge cable. The diagnosis strategy based on cable force and stress distribution state of steel wires was put forward. By establishing the bridge-cable and cable-steel wires model, the broken wires sample database was simulated numerically. A method of the characterization cable state pattern which can both represent the degree and location of broken wires inside a cable was put forward. The training and predicting results of the sample database by the back propagation （BP） neural network showed that the proposed broken wires diagnosis method was feasible and expanded the broken wires diagnosis research area by using the smart cable which was used to be only representing cable force.

关 键 词：Fiber optical Bragg grating sensor bridge cable broken wires diagnosis smart cable BP neuralnetwork 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM248[自动化与计算机技术—控制科学与工程]