| 摘要: |
| 提出一种基于MAG焊过程可闻电弧声信号采集和处理的熔透状态在线监测方法.通过对平板拼焊射流过渡过程中典型状态下的电弧声信号的实时采集与分析,采用小波去噪和短时加窗等预处理手段,提取了11个可表征焊缝熔透状态的特征参数.通过对构造的高维联合特征向量进行基于特征级的PCA参数融合,重新合成并选取了携带最多熔透状态信息量的8维特征向量,并以此为输入和四种熔透状态为输出,建立了BP和RBF熔透状态辨识网络模型.监测模型的应用例证表明,所建立的两种网络均可实现对熔透状态的在线识别,RBF网络的识别准确率高于BP网络6.25个百分点之多,其熔透状态整体辨识准确率达到91.25%. |
| 关键词: 电弧声 MAG焊 熔透状态 模式分类 神经网络 |
| DOI： |
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| 基金项目:宁波自然基金资助项目(2008A610031);黑龙江省自然基金资助项目(E2007-01);黑龙江省青年骨干教师基金资助项目(1153G009);哈尔滨市科技创新基金资助项目(2007RFQXG055) |
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| On-line monitoring of penetration status based on characteristic analysis of arc sound signal in MAG welding |
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BI Shujuan1,2, LAN Hu3, LIU Lijun3
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1.College of Mechanical and Electrical Engineering, Northeast Forestry University, Har-bin 150040, China;2.College of Mathematics and Computer, Harbin Institute, Harbin 150086, China;3.Harbin University of Science and Technology, Harbin 150080, China
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| Abstract: |
| Based on acquisition and processing of audible arc sound signal in the process of MIC welding,an online test method of penetration status was proposed.Arc sound signal un-der typical penetration status in the flat butt welding process with spray transfer was acquired and analyzed in real time.11 charac-teristic parameters were extracted to characterize welding penetra-tion status by wavelet denoising and short-time windowing tech-nology.PCA parameter was synchronized based on high-dimen-sion characteristic vector,8 dimensions characteristic vector with most information of penetration status was re-synthesized and taken as input parameters,four penetratioa statuses were taken as export parameters,and network models of BP and RBF to i-dentify the penetration status were established.The application of test model shows that the two networks can realize the online recognition of penetration status.The accuracy of RBF network reaches 91.25%,which is 6.25% more than that of BP. |
| Key words: arc sound MAC welding penetration status pattern classification neural network |
