【作者】 赵佳；

【导师】 于洋；

【作者基本信息】 沈阳工业大学 ， 精密仪器及机械， 2012， 硕士

【摘要】 石油已经成为人类社会不可缺少的资源,因此如何安全地储存石油受到人们越来越多的关注。大型油罐是用来储存石油的主要设备,所以非常有必要保证储油罐的安全。储罐罐底是储罐中最难检测的部分,目前常用的检测方法有:超声波检测、射线检测、涡流检测、漏磁检测、磁粉检测、渗透检测等,然而这些传统的检测方法耗时长,费用高,而且不能对储罐进行实时的检测,而声发射检测技术具有这些传统的检测方法无法比拟的优点,正逐渐被广泛应用。本文研究了声发射检测技术在储罐底板腐蚀检测中的应用,并对产生的信号进行了处理。本文介绍了罐底腐蚀声发射检测的基本原理,研究了罐底腐蚀声发射信号的类型、产生原因及其影响因素,并对检测过程中的噪声信号类型进行了分析和总结,以及借鉴了几种排除噪声信号的方法,如频率鉴别、幅度鉴别、软件鉴别和空间鉴别。同时通过对比多种不同的现代信号处理方法,最终通过基于独立分量分析的fastICA算法对罐底腐蚀声发射信号进行降噪研究,仿真过程实现了对双指数模型声发射信号与噪声信号混合信号的分离,从分离结果可以看出fastICA算法能够有效地区分这两种信号,验证了该算法的有效性。本文利用了PCI-2声发射采集系统进行实验研究,搭建了低碳钢钢板腐蚀声发射实验平台,配置了10%的FeCl3·6H2O溶液作为腐蚀溶液。在实验的过程中,需要实时地记录腐蚀声发射信号的波形,目的是通过对腐蚀声发射信号分析和处理得到腐蚀声发射信号具体的信息。实验结果表明,腐蚀声发射信号可分为:突发型、连续型及混合型,且腐蚀声发射信号的事件数随着腐蚀的持续进行呈现不同的趋势,可以明显地观察到腐蚀声发射信号的事件数随着腐蚀的深入而增加。该实验结论对实际的罐底腐蚀声发射检测有一定的借鉴价值。更多还原

【Abstract】 Oil has become a kind of indispensable resource to human society, so people pay more and more attention to the problem of storage safety. The main device for oil storage is large tank, therefore, safety detection of tank is very important. Bottom part of tank is most difficult to be detected, the most commonly used methods are: ultrasonic detection, eddy current detection, magnetic flux leakage detection, magnetic particle detection, penetrant detection, radiation detection and so on, however, these traditional methods have such disadvantages as: time-consuming, high cost, and can not achieve real time detection. Acoustic emission detection technology can overcome these disadvantages, which is also an non-destructive testing technology and gradually put into wide use. Therefore, this paper studies the application of acoustic emission detection in corrosion detection of tank bottom, and processes signals from experiment.This paper introduces basic principles of tank bottom corrosion acoustic emission detection and studies types, causes and influence factors of tank bottom corrosion acoustic emission signals. During detection processing noise signal types are summarized, kinds of methods for denoising noise signals are also introduced, such as frequency identification, amplitude identification, software identification and space identification. At the same time, by comparing different modern signal processing methods, independent component analysis is finally used and fastICA algorithm is used to achieve denoising of tank corrosion acoustic emission signals. Finally, it is used to achieve separation of mixture composed of double exponential model acoustic emission signal. Separation results demonstrate that fastICA algorithm can effectively separate the mixture.PIC-2 acoustic emission acquisition system is used to conduct experiment study and corrosion acoustic emission platform is built in this paper. The experiment uses 10% FeCl3·6H2O solution as corrosion solution. During experiment process, waveforms of corrosion acoustic emission signals are recorded real-time in order to be processed in order to obtain exact information. Experiment results demonstrate that: corrosion acoustic emission signals can be divided into three types: burst, continuous and mixed, and events of acoustic emission signals show different trends during the corrosion continues, and with the increasement of corrosion time, the number of acoustic emission events also increase. The experiment result can be a reference of corrosion acoustic emission detection for actual tank bottom.更多还原