【摘要】 声发射储罐罐底腐蚀检测过程中,采集到的腐蚀信号里不可避免地混有干扰。针对该问题,提出了基于极限学习机(ELM)的石油储罐罐底腐蚀信号识别方法。为验证该方法的有效性,在秦皇岛输油站的消防水罐里,进行模拟罐底腐蚀检测实验,并应用ELM对采集到的声发射信号进行分类识别,用b值法对ELM的分类效果进行评估。实验结果表明:ELM识别正确率高于90%。ELM识别出的腐蚀信号的b值变化规律与实验室条件下腐蚀信号的b值变化规律一致,并能够反映磷酸腐蚀碳钢板的过程。更多还原

【Abstract】 Collected corrosion signals are mixed with interference inevitably in acoustic emission( AE) detection process. To solve this problem,a novel extreme learning machine( ELM)-based method for corrosion signals recognition in AE testing of storage tank bottom was proposed. In order to test the validity of the method,the simulated tests of storage tank bottom corrosion were performed in the fire protection water tank of the oil transportation station in Qinhuangdao.ELM was applied in the classification of the collected corrosion signals,and b-value method was used to evaluate the classification effect of ELM. The experimental results indicated that the classification accuracy of ELM is above 90%; the b-value distribution of corrosion signals identified with ELM agrees well with that of corrosion signals in a laboratory;furthermore,the statistical distribution of b-value reflects the process of carbon steel sheet corroded by phosphoric acid.更多还原