The Design of Coil Array Sensor Based on Eddy Current Effect and Neural Network Detection Algorithm
-
摘要: 针对传统混凝土内部检测方式，只能测量混凝土保护层厚度，而无法同时对钢筋直径进行有效测量的问题，提出了一种基于脉冲涡流阵列的新型检测装置。使用ANSOFT仿真软件对检测模型进行仿真分析，并通过试验，验证了该检测模型对于混凝土保护层厚度以及钢筋直径具有较好的响应效果。通过BP神经网络建立了线圈信号和保护层厚度、钢筋直径之间的数学模型。试验结果基本满足精度要求，弥补了传统涡流检测仪器无法同时完成对保护层厚度与钢筋直径测量的不足。Abstract: In respect that the diameter of steel bar inside of concrete could not be measured effectively in traditional method, a new type of detection device based on pulsed eddy current array was proposed to detect both the thickness of concrete protective layer and the diameter of steel bar simultaneously. The detection model was simulated by ANSOFT simulation software, and physical experiments verified that the model had a good response to the thickness of the concrete protective layer and the diameter of the steel bar. Then, the BP neural network was used to establish the mathematical relationship of the coil signal to the thickness the protective layer and the diameter of the steel bar. The result of neural network experiment satisfied with the requirement basically. The new type of detection device could measure both the thickness of concrete protective layer and the diameter of steel bar at the same time, which the traditional eddy current detection equipment could not complete.
-
-
[1] 王茹, 邵珍奇. 雷达技术在混凝土结构无损检测中的应用[J]. 核电子学与探测技术, 2009, 29(2):459-462. [2] 程守洙, 江之永. 普通物理学[M].北京:高等教育出版社, 2006. [3] SILVESTER P. Eddy-current modes in linear solid-iron bars[J].Proceedings of the Institution of Electrical Engineers, 2010, 112(8):1589-1594.
[4] 李天博, 刘守华, 陈坤华. 脉冲涡流阵列检测系统仿真与实验[J]. 磁性材料及器件, 2013(6):26-30. [5] TIAN L, YIN C, CHENG Y, et al. Successive approximation method for the measurement of thickness using pulsed eddy current[C]//Instrumentation and Measurement Technology Conference.[S.l.]:[s.n.], 2015:848-852.
[6] 周德强, 潘萌, 常祥,等. 铁磁性构件缺陷的脉冲涡流检测模式研究[J]. 仪器仪表学报, 2017, 38(6):1498-1505. [7] 张震. 脉冲涡流测厚技术研究[D]. 西安:西安理工大学, 2007. [8] BUCK J A, UNDERHILL P R, MORELLI J E, et al. Simultaneous multiparameter measurement in pulsed eddy current steam generator data using artificial neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(3):672-679.
[9] 熊亮, 赵俊锴. 基于RBF神经网络的变电站混凝土立柱抗压强度评定[J].无损检测,2015,37(5):52-54. [10] 刘奕君, 赵强, 郝文利. 基于遗传算法优化BP神经网络的瓦斯浓度预测研究[J]. 矿业安全与环保, 2015,42(2):56-60.
计量
- 文章访问数: 0
- HTML全文浏览量: 0
- PDF下载量: 2
下载:
