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Cluster analysis of acoustic emission signals during tank bottom steel pitting corrosion process
BI Haisheng1, LI Zili2, HU Dedong1, LUO Qin3, ISAAC Toku-Gyamerah2, WU Xiangyang4
(1.College of Electromechanical Engineering in Qingdao University of Science & Technology, Qingdao 266061, China;2.College of Pipeline and Civil Engineering in China University of Petroleum, Qingdao 266580, China;3.Digitalization Engineering Department,SINOPEC Petroleum Engineering Corporation, Dongying 257000, China;4.Institute of Exploration and Development in Xingzichuan Oil Production Plant,Yanchang Oilfield,Yanan 717400,China)
Abstract:
The pitting characteristics of tank bottom steel sample were studied by combined acoustic emission(AE)and electrochemical techniques in acidic NaCl solution (w=3.0%, pH=2.0). The AE signals characteristic parameters were classified using K-means clustering algorithm and each cluster signal characteristic was also extracted. The classified signals were trained using BP artificial neural network,and the AE signals from parallel experiments were successfully identified. The results show that the oscillation, movement and burst of hydrogen bubbles, breakage of passive film, growth and propagation of pit are the typical AE sources in pitting, which could be effectively classified using cluster analysis and identified by artificial neural network. It has guiding significance for interpreting and evaluating the AE on-site testing result of bottom corrosion of atmospheric storage tank, improving the reliability of testing result, reducing risk and ensuring the safety of tank.
Key words:  tank bottom steel  pitting corrosion  acoustic emission  K-means clustering  Gabor wavelet transform