摘要
为检测深海科考设备外壳钢板腐蚀缺陷,首先设计了搭载三维霍尔传感器直线阵列的漏磁信号检测小车,对人工制造的矩形凹坑缺陷样本进行磁化并检测漏磁信号;将漏磁信号划分为主漏磁信号和次漏磁信号,提取主漏磁信号曲线的波峰高度、波形宽度、包络面积、周长、图形形心、峰谷宽度等几何特征,以及次漏磁信号的阈值宽度、环向微分宽度等磁场变化特征;对Y向主漏磁信号的峰谷距离与矩形缺陷长度进行线性拟合;采用反向传播(BP)神经网络建立矩形凹坑缺陷宽度、深度的计算模型。实验结果表明:该方法能够有效挖掘漏磁信号中隐含特征参量,缺陷长度、宽度、深度的量化平均绝对误差分别为0.63,0.51,0.85 mm,满足检测精度要求。
In order to detect the corroded defects of the deep-sea scientific equipment,a magnetic flux leakage(MFL)signal measurement system which has the three-dimensional(3D)Hall sensor linear array is developed firstly.For a series of artificial pits locally magnetized,3D MFL signals are measured synchronously.3D MFL signals from the array are divided into primary MFL signal and secondary MFL signal.For the primary MFL,extract geometrical characteristics such as crest height,waveform width,envelop area,perimeter,centroid,the distance between crest and trough,etc.For the secondary MFL signal,threshold width and the circular differential width.Then linear fitting is carried out on crest and trough of the Y axis signal and the length of the defect.Finally,the BP neural network model is used to calculate the width and depth of the defects.The experimental results show that the method can mine the concealed features within the MFL signals and the quantization mean absolute errors of the length,width and depth are 0.63 mm,0.51 mm and 0.85 mm,respectively.
作者
巩文东
杨涛
连超
张宏杰
GONG Wendong;YANG Tao;LIAN Chao;ZHANG Hongjie(Shandong Polytechnics,Jinan 250105,China;School of Mechanical Engineering,Tiangong University,Tianjin 300387,China;Institute of Oceanology,Chinese Academy of Sciences,Qingdao 266071,China)
出处
《传感器与微系统》
CSCD
2020年第10期115-118,122,共5页
Transducer and Microsystem Technologies
基金
中国科学院A类战略性先导科技专项项目(XDA19060402,XDA22050302)
大科学研究中心“科学”号科考船高端用户项目(KEXUE2019G05)
山东省高校科研计划项目(J18KB030)。
