平板导电结构缺陷脉冲涡流和超声复合检测方法
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摘要
面向平板导电结构不同深度缺陷检测需求,针对脉冲涡流和超声单一检测方法能力受限,即脉冲涡流对深层缺陷检测能力降低与超声对表面和近表面缺陷检测效果不佳的问题,提出利用两传感器信息互补的Dempster-Shafter(D-S)证据理论复合检测方法.针对脉冲涡流和超声两种检测方式适用检测区域不同而引起的证据冲突问题,研究加权分配方法加以解决.对于单传感器检测过程中可能存在误报情况的问题,研究将实际误报率考虑在内的贝叶斯推理方法以求得单一传感器检测结果的基本概率分配函数并作为D-S证据.将带有不同深度缺陷的平板导电结构作为实验对象,通过单一传感器检测、贝叶斯估计、D-S证据理论方法进行不同深度位置的缺陷检测,结果表明,使用引入加权分配的D-S证据推理方法时,缺陷检测准确性和检测范围均有所提高.
A complex detection method which combines plused eddy current(PEC) and ultrasonic testing(UT) based on the Dempster-Shafter(D-S) evidence theory is proposed to detect the defects in the planar conductive structures at different depth locations. It intends to overcome the testing limitation that PEC testing is poor in deep defects detection and UT performes bad in surface and near-surface defects respectively. To solve the conflict problem of different detection ranges of PEC and UT, the weighted distribution processing is studied and added to the D-S evidence theory. To consider the posibility of false positives in single sensor detection, the evidence, i.e. posibility of defect from single sensor, is improved by using the Bayesian inference method. The experiment is carried out on a planar conductive structure testing sample with different depths of the defects. By comparing with the single sensor testing and Bayesian estimation method,results show that the proposed technique using D-S evidence theory with the weighted distribution processing in the combined testing obtains better defect detection result and wider testing scale.
A complex detection method which combines plused eddy current(PEC) and ultrasonic testing(UT) based on the Dempster-Shafter(D-S) evidence theory is proposed to detect the defects in the planar conductive structures at different depth locations. It intends to overcome the testing limitation that PEC testing is poor in deep defects detection and UT performes bad in surface and near-surface defects respectively. To solve the conflict problem of different detection ranges of PEC and UT, the weighted distribution processing is studied and added to the D-S evidence theory. To consider the posibility of false positives in single sensor detection, the evidence, i.e. posibility of defect from single sensor, is improved by using the Bayesian inference method. The experiment is carried out on a planar conductive structure testing sample with different depths of the defects. By comparing with the single sensor testing and Bayesian estimation method,results show that the proposed technique using D-S evidence theory with the weighted distribution processing in the combined testing obtains better defect detection result and wider testing scale.
引文
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[14]李国厚.导电结构涡流/超声检测与评估技术的研究[D].杭州:浙江大学,2011.(Li G H.Research on eddy current/ultrasonic testing and evaluation of conductive structures[D].Hangzhou:Zhenjiang University,2011.)
[15]Habibalahi A,Safizadeh M S.Pulsed eddy current and ultrasonic data fusion applied to stress measurement[J]Measurement Science&Technology,2014,25(5):1009-1016.
[16]Heideklang R,Shokouhi P.Multi-sensor image fusion at signal level for improved near-surface crack detection[J]NDT&E Int,2015,71:16-22.
[17]Szugs T,Krüger A,Jansen G,et al.Combination of ultrasonic and eddy current testing with imaging for characterization of rolling contact fatigue[C].The 19th World Conf on Non-Destructive Testing.Munich:NDT2016:1-8.
[18]Fioretti G.A mathematical theory of evidence for G.L.Sshackle[J].Mind&Society,2001,2(1):77-98.
[19]Shi F,Su X,Qian H,et al.Research on the fusion of dependent evidence based on rank correlation coefficient[J].Sensors(Basel),2017,17(10):2362-2376.
[20]徐小力,刘秀丽,蒋章雷,等.基于主观贝叶斯推理的多传感器分布式故障检测融合方法[J].机械工程学报,2015(7):91-98.(Xu X L,Liu X L,Jiang Z L,el al.The fusion fault detection method of multi-sensor distributed based on subjective bayesian inference[J].J of Mechanical Engineering,2015(7):91-98.)
[21]张品,董为浩,高大冬.一种优化的贝叶斯估计多传感器数据融合方法[J].传感技术学报,2014(5):643-648.(Zhang P,Dong W H,Gao D D.An optimal method of data fusion for multi-sensors based on bayesian estimation[J].Chinese J of Sensors and Actuators2014(5):643-648.)
[2]De S,Gupta K,Stanley R J,et al.A comrehensive multi-modal NDE data fusion approach for failure assessment in aircraft lap-joint mimics[J].IEEE Trans on Instrumentation and Measurement,2013,62(4):814-827.
[3]Rosado L S,Ramos P M,Piedade M.Real-time processing of multifrequency eddy currents testing signals:Design,implementation,and evaluation[J].IEEETrans on Instrumentation and Measurement,2014,63(5):1262-1271.
[4]Gao P,Wang C,Li Y,et al.Electromagnetic and eddy current NDT in weld inspection:Areview[J].Insight-Non-Destructive Testing and Condition Monitoring,2015,57(6):337-345.
[5]唐华溢.涡流与电磁超声复合无损检测技术研究[D]杭州:浙江大学,2014.(Tang H Y.Research on composite non-destructive detection technology of ECT and EMAT[D].Hangzhou:Zhejiang University,2014.)
[6]Xie S,Tian M,Pan X,et al.A hybrid nondestructive testing method of pulsed eddy current testing and electromagnetic acoustic transducer techniques for simultaneous surface and volumetric defects inspection[J].NDT&E Int,2017,86:153-163.
[7]Edwards R S,Sophian A,Dixon S,et al.Dual EMAT and PEC non-contact probe:Applications to defect testing[J]NDT&E Int,2006,39(1):45-52.
[8]He Y,Pan M,Chen D,et al.PEC defect automated classification in aircraft multiply structures with interlayer gaps and lift-offs[J].NDT&E Int,2013,53:39-46.
[9]Sophian A,Tian G,Fan M.Pulsed eddy current non-destructive testing and evaluation:A review[J]Chines J of Mechanical Engineering,2017,30(6):500-514.
[10]Heideklang R,Shokouhi P.Application of data fusion in nondestructive testing(NDT)[C].Int Conf on Information Fusion.Istanbul:IEEE,2013:835-841.
[11]Fan M,Cao B,Sunny A I,et al.Pulsed eddy current thickness measurement using phase features immune to liftoff effect[J].NDT&E Int,2017,86:123-131.
[12]Andruschak N,Saletes I,Filleter T,et al.An NDT guided wave technique for the identification of corrosion defects at support locations[J].NDT&E Int,2015,75:72-79.
[13]Pan M,He Y,Tian G,et al.PEC frequency band selection for locating defects in two-layer aircraft structures with Air gap variations[J].IEEE Trans on Instrumentation&Measurement,2013,62(10):2849-2856.
[14]李国厚.导电结构涡流/超声检测与评估技术的研究[D].杭州:浙江大学,2011.(Li G H.Research on eddy current/ultrasonic testing and evaluation of conductive structures[D].Hangzhou:Zhenjiang University,2011.)
[15]Habibalahi A,Safizadeh M S.Pulsed eddy current and ultrasonic data fusion applied to stress measurement[J]Measurement Science&Technology,2014,25(5):1009-1016.
[16]Heideklang R,Shokouhi P.Multi-sensor image fusion at signal level for improved near-surface crack detection[J]NDT&E Int,2015,71:16-22.
[17]Szugs T,Krüger A,Jansen G,et al.Combination of ultrasonic and eddy current testing with imaging for characterization of rolling contact fatigue[C].The 19th World Conf on Non-Destructive Testing.Munich:NDT2016:1-8.
[18]Fioretti G.A mathematical theory of evidence for G.L.Sshackle[J].Mind&Society,2001,2(1):77-98.
[19]Shi F,Su X,Qian H,et al.Research on the fusion of dependent evidence based on rank correlation coefficient[J].Sensors(Basel),2017,17(10):2362-2376.
[20]徐小力,刘秀丽,蒋章雷,等.基于主观贝叶斯推理的多传感器分布式故障检测融合方法[J].机械工程学报,2015(7):91-98.(Xu X L,Liu X L,Jiang Z L,el al.The fusion fault detection method of multi-sensor distributed based on subjective bayesian inference[J].J of Mechanical Engineering,2015(7):91-98.)
[21]张品,董为浩,高大冬.一种优化的贝叶斯估计多传感器数据融合方法[J].传感技术学报,2014(5):643-648.(Zhang P,Dong W H,Gao D D.An optimal method of data fusion for multi-sensors based on bayesian estimation[J].Chinese J of Sensors and Actuators2014(5):643-648.)