基于腐蚀图像与支持向量机的CO_2腐蚀类型识别方法研究  

Study on CO_2 Corrosion Recognition Method Based on SVM

作　　者：陈桂娟[1] 贾春雨[2] 邹龙庆[1] 付海龙[1] 

机构地区：[1]东北石油大学机械科学与工程学院 [2]大庆石化公司

出　　处：《化工机械》2014年第6期742-745,共4页Chemical Engineering & Machinery

基　　金：国家科技支撑计划项目(2012BAH28F03);黑龙江省教育厅科学技术研究重点项目(12521Z007);黑龙江省自然基金项目(E201335);东北石油大学青年基金(ky120221)

摘　　要：针对CO2腐蚀过程复杂、腐蚀类型特征难以提取和准确识别的问题,提出了以腐蚀图像信息为特征、支持向量机(SVM)为识别器的CO2腐蚀类型识别方法。以N80钢CO2腐蚀表面形貌图像为对象,经灰度处理、灰度增强,再使用小波分割形成子图像,提取其能量信息作为特征向量。以未腐蚀、点腐蚀和均匀腐蚀3种类型样本集构建支持向量机分类器,经测试,该方法可准确识别CO2腐蚀类型,并通过与神经网络分类器的对比,验证了该方法的优越性。Aiming at the difficulty in recognizing the CO2 corrosion types and extracting their corrosion charac- teristics, a new identification method which basing on the corrosion images and taking SVM as a recognizer was proposed. Taking N80 steel＇s C02-corroded surface morphology images as the object, the wavelet can decom- pose these images into sub-images through gray processing and gray enhancement so as to extract their energy information as the eigenvector. Based on the sample sets of without corrosion, pitting corrosion and uniform corrosion, having SVM classifier constructed shows that this method can recognize CO= corrosion types accu- rately, and comparing with the neural network classifier verifies its superiority.

关 键 词：CO2腐蚀 支持向量机 腐蚀形貌 灰度处理 小波分析 

分 类 号：TQ050.9[化学工程]