Cluster analysis of acoustic emission signals in pitting corrosion of low carbon steel
Cluster analysis of acoustic emission signals in pitting corrosion of low carbon steel
复制标题
低碳钢点蚀声发射信号聚类分析
DOI：
10.1002/mawe.201500347
复制
发表时间：
2015-07-01
影响因子：
1.1
通讯作者：
Cheng, Y.
中科院分区：
文献类型：
--
作者：
Bi, H.;Li, Z.;Cheng, Y.
采用声发射（AE）和电化学技术研究了低碳钢试样在3.0 wt.% NaCl酸化至pH 2.0溶液中的点蚀特性。采用K均值聚类算法对多点腐蚀产生的声发射信号进行分类，然后对每个分类信号进行声发射参数相关图和随时间分布分析。在此基础上，利用Gabor小波变换在时域和频域提取各声源特征。根据分类信号训练误差反向传播（BP）人工神经网络，成功识别平行实验声发射信号。实验结果表明，氢泡活化、氧化膜破裂和坑生长是点蚀过程中典型的声发射源，可以通过聚类分析和反向传播神经网络进行有效的分类和识别。将实验室检测数据与声发射在线检测数据相结合，有助于评估储罐底部腐蚀严重程度，解释腐蚀来源，进一步提高现场检测的可靠性，降低风险。
The pitting corrosion characteristics of low carbon steel specimens are studied by acoustic emission (AE) and electrochemical techniques, in a 3.0 wt.% NaCl solution acidified to pH 2.0. The acoustic emission signals generated by pitting corrosion are classified based on multiple acoustic emission parameters using K‐means clustering algorithm, then each classified signals are analyzed by acoustic emission parameters correlation plot and distribution with time. Furthermore, each acoustic source characteristics is extracted using Gabor wavelet transform (WT) in the time and frequency domain. An error back propagation (BP) artificial neural network (ANN) is trained according to the classified signals, so as to successfully identify the acoustic emission signals from parallel experiments. Experimental results show that the hydrogen bubble activation, oxidized film rupture and pit growth are typical acoustic emission sources in pitting corrosion process, which can be effectively classified by cluster analysis and recognized by back propagation neural network. The data gathered from laboratory tests combined with the real data from acoustic emission on‐line storage tank floor inspection can help to evaluate the bottom corrosion severity and interpreter the corrosion source, further to make the on‐site testing more reliable and reduce the risk.