| 储罐底板腐蚀状态的人工神经网络智能评价方法 |
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| 引用本文： | 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(6):5-7,11. |
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| 作者姓名： | 戴光 邱枫 陈荣刚 张颖 粟辉霖 |
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| 作者单位： | [1]东北石油大学,大庆163318 [2]南通华盛港口有限公司,南通226500 |
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| 基金项目： | 黑龙江省教育厅科学技术研究项目（12511008） |
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| 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。
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| 关 键 词： | 储罐底板腐蚀 声发射在线检测 外观检查因素 人工神经网络 |
Artificial Neural Network Intelligent Evaluation Method of Tank Bottom Corrosion Status |
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| 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(6):5-7,11. |
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| Authors: | DAI Guang QIU Feng CHEN Rong-Gang ZHANG Ying SU Hui-Lin |
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| Affiliation: | 1. Northeast Petroleum University, Daqing 163318, China; 2. Nantong Huasheng Harbor Limited Company, Nantong 226500, China) |
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| 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. |
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| Keywords: | Tank bottom corrosion Acoustic emission online detection Appearance inspection factors Artificial neural network |
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