Artificial Neural Network Intelligent Evaluation Method of Tank Bottom Corrosion Status
摘 要
根据储罐底板在线检测的声发射信息和外观检查信息,确定与储罐底板腐蚀状态相关的表征因素,应用人工神经网络智能评价方法,分别建立基于外观检查信息、基于声发射信息和基于在线检测信息的储罐底板腐蚀状态评价模型。通过对测试样本的评价,对比声发射检测评价结果,其中基于在线检测信息的储罐底板腐蚀状态评价模型的准确率为94%,该模型能够对储罐底板腐蚀状态进行准确的评价,实现储罐底板声发射在线检测评价的智能化。
Abstract
According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status were confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information and online testing information were established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information was 94%. The evaluation model could evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.
中图分类号 TG115.28
所属栏目 科研成果与学术交流
基金项目 黑龙江省教育厅科学技术研究项目(12511008)
收稿日期 2011/8/19
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备注戴光(1954-),男,系主任,博士,教授,主要从事化工过程机械和无损检测教学和科研工作。
引用该论文: DAI Guang,QIU Feng,CHEN Rong-Gang,ZHANG Ying,SU Hui-Lin. Artificial Neural Network Intelligent Evaluation Method of Tank Bottom Corrosion Status[J]. Nondestructive Testing, 2012, 34(6): 5~7
戴光,邱枫,陈荣刚,张颖,粟辉霖. 储罐底板腐蚀状态的人工神经网络智能评价方法[J]. 无损检测, 2012, 34(6): 5~7
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参考文献
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【2】E 1930-02Standard Test Method for Examination of Liquid-Filled Atmospheric and Low-Pressure Metal Storage Tanks Using Acoustic Emission[S].
【3】吴欣怡,黄松岭,赵伟.使用改进型BP神经网络量化裂纹漏磁信号[J].无损检测,2009,31(8):603-605.
【4】魏碧霞,杨晓翔,郭金泉,等.基于神经网络的模糊风险评价技术的应用[J].油气储运,2009,28(2):20-23.
【5】陈孝趋,鲁聪达,廖枝平.BP算法的改进及其在Matlab上的实现[J] .控制工程,2005,12(5):96-98.
【6】JB/T 10764—2007无损检测 常压金属储罐声发射检测及评价方法[S].
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