| 基于改进的支持向量回归机算法的磁记忆定量化缺陷反演 |
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| 引用本文： | 李思岐, 俞洋, 党永斌, 陈思雨, 冷建成. 基于改进的支持向量回归机算法的磁记忆定量化缺陷反演[J]. 工程科学学报, 2018, 40(9): 1123-1130. DOI: 10.13374/j.issn2095-9389.2018.09.014 |
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| 作者姓名： | 李思岐 俞洋 党永斌 陈思雨 冷建成 |
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| 作者单位： | 哈尔滨工业大学航天学院,哈尔滨,150001;哈尔滨工业大学电气工程及自动化学院,哈尔滨,150001;东北石油大学机械科学与工程学院,大庆,163318 |
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| 基金项目： | 国家自然科学基金资助项目(11272084,61571161,11472076)东北石油大学研究生创新科研资助项目(YJSCX2016-024NEPU)中国石油科技创新基金资助项目(2015D-5006-0602)中国石油和化学工业联合会科技指导资助项目(2017-01-05) |
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| 摘 要： | 针对焊缝缺陷磁记忆检测中存在定量化反演难题,建立了基于改进的支持向量回归机定量反演模型.以预制不同尺寸未焊透和夹渣缺陷的Q235焊接试样为试验材料,进行磁记忆扫描检测发现:缺陷位置的磁记忆信号特征参数随尺寸变化而呈现一定的变化规律,但同时存在分散性和不确定性.鉴于磁记忆信号样本的有限性、分散性和非线性,首先将提取到的磁记忆特征参数进行归一化处理,引入支持向量回归机建立焊缝缺陷磁记忆定量反演模型,并进一步利用模拟退火算法对支持向量回归机参数进行优化,使目标函数达到全局最优而非局部最优.最后,考虑到由磁记忆信号逆向反推缺陷的三维尺寸,存在解的不确定性,为此在缺陷单维尺寸反演模型的基础上,通过构建多层结构的支持向量回归机进行多尺寸反演输出,建立了基于模拟退火支持向量回归机的焊缝缺陷磁记忆定量反演模型,结果表明:未焊透缺陷尺寸反演最大相对误差为7.96%,夹渣缺陷为4.97%,为焊缝缺陷的磁记忆反演与定量化评价提供一种新的思路.
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| 关 键 词： | 焊缝缺陷 反演 磁记忆检测 支持向量回归机 模拟退火算法 |
| 收稿时间： | 2017-12-29 |
Metal magnetic memory quantitative inversion of defects based onoptimized support vector machine regression |
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| LI Si-qi, YU Yang, DANG Yong-bin, CHEN Si-yu, LENG Jian-cheng. Metal magnetic memory quantitative inversion of defects based onoptimized support vector machine regression[J]. Chinese Journal of Engineering, 2018, 40(9): 1123-1130. DOI: 10.13374/j.issn2095-9389.2018.09.014 |
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| Authors: | LI Si-qi YU Yang DANG Yong-bin CHEN Si-yu LENG Jian-cheng |
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| Affiliation: | 1) School of Astronautics, Harbin Institute of Technology, Harbin 150001, China2) School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China3) School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, China |
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| Abstract: | During welding processes, initial defects such as incomplete penetration and slag are easily generated. To ensure the safe operation of welding components, welded joints must be tested rigorously. Metal magnetic memory (MMM) technology, a new nondestructive testing in the 21st century, can detect macroscopic defects as well as early stress concentrations and hidden damages. However, the quantitative MMM testing is still a bottleneck for weld defects. To solve the bottleneck of quantitative inversion of weld defects by MMM testing, a quantitative inversion model was presented based on a support vector machine (SVM) method optimized with simulated annealing (SA) algorithm. Steel Q235 welded plate specimens, which were prefabricated with different sizes of incomplete penetration and slag defects, were tested. It is found that with the increase of weld damage degree, the peak-peak values of the tangential and normal magnetic field intensity exhibit nonlinear growth, as well as the change rates of the tangent and normal magnetic field intensity. In other words, the MMM feature parameters vary with the defect size, but the signals are scattered and uncertain. First, considering the finite, dispersive, and non-linear MMM signals, the MMM feature parameters data were normalized, and the MMM quantitative inversion model of weld defects was established based on SVM. Furthermore, the SVM parameters was optimized with SA so that the objective function of the model could reach the global optimal solution. Finally, considering the solution uncertainty when the three-dimensional sizes of weld defects were reversed from the MMM signals, a modified MMM multi-dimensional SVM inversion model was presented by constructing SVM multi-layer structures and optimized with SA. The results show that maximum inversion relative error of incomplete penetration defect size is 7.96%, and the defect of slag is 4.97%, which provides a new tool for quantitative MMM inversion and evaluation of weld defects. |
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| Keywords: | weld defects inversion magnetic memory testing support vector machine simulated annealing algorithm |
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