吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1854-1860.doi: 10.13229/j.cnki.jdxbgxb201706025

• 论文 • 上一篇    下一篇

基于模糊隶属度最大似然估计的焊缝隐性缺陷磁记忆信号识别

邢海燕1, 葛桦1, 李思岐2, 杨文光3, 孙晓军1   

  1. 1.东北石油大学 机械科学与工程学院, 黑龙江 大庆 163318;
    2.哈尔滨工业大学 航天学院,哈尔滨 150001;
    3.甘肃蓝科石化高新装备股份有限公司,兰州 730070
  • 收稿日期:2016-09-07 出版日期:2017-11-20 发布日期:2017-11-20
  • 作者简介:邢海燕(1971-),女,教授,博士.研究方向:石油石化装备损伤检测及可靠性评价,机械设备状态监测与故障诊断.E-mail:xxhhyyhit@163.com
  • 基金资助:
    中石油科技创新基金项目(2015D-5006-0602); 国家自然科学基金项目(11272084, 11472076,11072056); 东北石油大学研究生创新科研项目(YJSCX2016-024NEPU); 黑龙江省博士后科研启动基金项目(LBH-Q13035)

Hidden defect metal magnetic memory identification for welded joints based on fuzzy membership and maximum likelihood estimation

XING Hai-yan1, GE Hua1, LI Si-qi2, YANG Wen-guang3, SUN Xiao-jun1   

  1. 1.School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318,China;
    2.School of Astronautics, Harbin Institute of Technology, Harbin 150001,China;
    3.Lanpec Technologies Limited, Lanzhou 730070,China
  • Received:2016-09-07 Online:2017-11-20 Published:2017-11-20

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摘要： 针对磁记忆信号在焊缝残余应力集中、隐性损伤识别和定位上的难题,建立了模糊隶属度最大似然估计识别模型。通过疲劳载荷作用下的Q235钢板状焊缝试件磁记忆检测试验,研究了焊缝残余应力集中、隐性损伤直至宏观断裂整个过程的磁记忆信号变化规律,提取多种磁记忆特征值:峰峰值ΔHp(y)、梯度最大值Kmax、梯度均值Kave、极限状态系数最大值mmax,引入正态分布函数计算其概率,结合半梯形模糊隶属度函数,建立了焊缝隐性损伤的模糊隶属度最大似然估计识别模型,判断疑似缺陷点进而识别隐性损伤并定位缺陷。结果表明:基于多种特征参数基础上的最大似然模糊隶属度估计结果与实际缺陷位置完全一致,验证了该模型可以有效避免误判的发生,为实际工程中应用磁记忆技术进行焊缝隐性损伤识别和定位提供了新的方法。

关键词: 材料合成与加工工艺, 磁记忆检测, 焊缝隐性损伤, 隶属度函数, 最大似然估计

Abstract: To overcome the difficulty of Metal Magnetic Memory (MMM) technique in identification and location of the hidden defect in welded joints, a method of Maximum Likelihood Estimation (MLE) and fuzzy membership degree is put forward. The feature law of critical hidden damage is studied by fatigue experiment of Q235 steel plate with incompletely penetrated weld joint. Four feature parameters are extracted, that are peak to peak value ΔHp(y), mean gradient Kave, maximal gradient Kmax, and maximal limit coefficient mmax. The normal distribution function is employed to calculate the probabilities of the four feature parameters. Then, MLE values are obtained to establish the MLE fuzzy membership model. This model can be used to identify the suspected defect position. Results show that the MLE fuzzy membership model can effectively locate the weld defect and provide a new tool of identification and location of weld hidden damage with MMM method.

Key words: materials synthesis and processing technology, metal magnetic memory(MMM) testing, weld hidden damage, membership functions, maximum likelihood estimation

中图分类号: 

  • TG441.7
[1] Jiles D C. A new approach to modeling the magneto mechanical effect[J]. Journal of Applied Physics,2004,95(11):7058-7060.
[2] Doubov A A.Express method of quality control of a spot resistance welding with usage of metal magnetic memory[J].Thermal Engineering,1999,46(5):369-372.
[3] 邸新杰,李午申,白世武,等.金属磁记忆检测信号的二维谱熵特征[J].焊接学报,2006,27(11):69-72.
Di Xin-jie,Li Wu-shen,Bai Shi-wu,et al. Two-dimension spectrum entropy feature for metal magnetic memory signal[J]. Transactions of the China Welding Institution, 2006,27(11):69-72.
[4] 任吉林,孙金立,周培,等.磁记忆二维检测的研究与工程应用[J].机械工程学报,2013,49(22):21-23.
Ren Ji-lin,Sun Jin-li,Zhou Pei,et al. Engineering application prospect and research on magnetic memory in two-dimensional testing[J].Journal of Mechanical Engineering,2013,49(22):21-23.
[5] 王进,任吉林,任尚坤,等.基于金属磁记忆法的焊接缺陷特征值提取[J]. 无损检测,2009,31(12):940-942.
Wang Jin,Ren Ji-lin,Ren Shang-kun,et al. Feature extraction of welding defects based on metal magnetic memory[J]. Nondestructive Testing,2009,31(12):940-942.
[6] 张卫民,邱冬超,于霞,等.关于利用金属磁记忆方法进行应力定量化评价问题的讨论[J]. 机械工程学报,2015,51(8):21-24.
Zhang Wei-min,Qiu Dong-chao,Yu Xia,et al. Discussion on stress quantitative evaluation using metal magnetic memory method[J].Journal of Mechanical Engineering,2015,51(8):21-24.
[7] 刘红光,张卫民,毛新颜,等.基于磁记忆检测技术的焊接缺陷多参数识别系统[J]. 制造业自动化,2009,31(2):811-814.
Liu Hong-guang,Zhang Wei-min,Mao Xin-yan,et al. Multi-parameter identification system of weld defects based on magnetic memory testing[J].Manufacturing Automation,2009,31(2):811-814.
[8] 胥永刚,谢志聪,崔伶丽,等.基于ITD的齿轮磁记忆信号特征提取方法的研究[J]. 仪器仪表学报,2013,34(3):671-676.
Xu Yong-gang,Xie Zhi-cong,Cui Ling-li,et al. Study on feature extraction method of gear magnetic memory signal based on ITD[J]. Chinese Journal of Scientific Instrument,2013,34(3):671-676.
[9] 董丽虹,徐滨士,董世运,等.金属磁记忆技术表征应力集中、残余应力及缺陷的探讨[J]. 材料工程,2009(8):19-23.
Dong Li-hong, Xu Bin-shi, Dong Shi-yun,et al. Discussion on characterizing stress concentration residual stress and defect on metal magnetic memory testing[J]. Journal of Materials Engineering,2009(8):19-23.
[10] 邢海燕,葛桦,秦萍,等.基于遗传神经网络的焊缝缺陷等级磁记忆定量化研究[J]. 材料科学与工艺,2015,23(2):33-38.
Xing Hai-yan,Ge Hua,Qin Ping,et al. MMM quantifying of welded joint defect levels based on BP neural network optimized by genetic algorithm[J]. Materials Science& Technology,2015,23(2):33-38.
[11] Yamamura K. Bayes estimates as an approximation to maximum likelihood estimates[J]. Population Ecology,2016,58(1):45-52.
[12] 盛骤. 概率论与数理统计[M]. 北京:高等教育出版社,2008.
[13] 邢海燕,党永斌,王犇,等. 基于K-近邻隶属度模糊支持向量机的再造抽油杆损伤等级磁记忆定量识别[J]. 石油学报,2015,36(11):1427-1432,1456.
Xing Hai-yan,Dang Yong-bin,Wang Ben,et al. Quantitative MMM identification of damage levels based on KNN FSVM for remanufactured sucker rod[J]. ACTA Petrolei Sinica,2015,36(11):1427-1432,1456.
[14] 陈涛,李智,莫玮. 基于模糊模式识别的爆炸物THz光谱识别[J]. 仪器仪表学报,2012,33(11):2480-2486.
Chen Tao, Li Zhi, Mo Wei. Identification of terahertz absorption spectra of explosives based on fuzzy pattern recognition[J]. Chinese Journal of Scientific Instrument,2012,33(11):2480-2486.
[15] 郝建坤, 黄玮, 刘军, 等. 空间变化PSF非盲去卷积图像复原法综述[J]. 中国光学, 2016, 9(1): 41-50.
Hao Jian-kun, Huang Wei, Liu Jun, et al. Review of non-blind deconvolution image restoration based on spatially-varying PSF[J]. Chinese Optics, 2016, 9(1): 41-50.
[16] 侯天娇, 郝志成, 朱明, 等. 一种基于FPGA的彩色图像实时增强方法[J]. 液晶与显示, 2016, 31(12): 1161-1167.
Hou Tian-jiao, Hao Zhi-cheng, Zhu Ming, et al. Real-time color image enhancement method based on FPGA[J].Chinese Journal of Liquid Crystal and Displays, 2016, 31(12): 1161-1167.
[17] 许廷发, 罗璇, 苏畅, 等. 多帧距离选通图像点扩散函数估计的超分辨率重建[J]. 中国光学, 2016, 9(2): 226-233.
Xu Ting-fa, Luo Xuan, Su Chang, et al. Super-resolution reconstruction of multiformat laser range-gated image by the estimate of point-spread function[J]. Chinese Optics, 2016, 9(2): 226-233.
[18] 刘亮,靳斌,初战平. 基于梯形隶属度模糊算法对电能质量的判断[J]. 电力学报,2014,29(2):110-114.
Liu Liang, Jin Bin, Chu Zhan-ping. Based on trapezoidal fuzzy algorithm to determine the quality of electric energy[J]. Journal of Electric Power,2014,29(2):110-114.
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