摘要
针对漏磁检测中漏磁信号识别的问题,引入DKSVD字典学习方法来识别缺陷的种类。将实验采集到的数据制作成有标签的数据集,通过OMP算法和SVD算法迭代的优化字典和稀疏系数,构造出最优字典,再用构造出的字典原子重新组合来表示测试集的数据,判别出测试集样本类别。实验证明字典学习方法能够基于训练集的特征重构漏磁信号,对漏磁信号有良好的识别能力。在不同数据维度下,通过和支持向量机(SVM)算法识别效果进行对比,DKSVD算法取得了更好的效果。
In order to solve the problem of defect identification in magnetic flux leakage testing, DKSVD dictionary learning method is introduced. By means of OMP algorithm and SVD algorithm to optimize the dictionary and the sparse coefficient, the optimal dictionary is constructed, and then the constructed dictionary atoms are combined to represent the data of the test set. The experimental results show the feasibility of the dictionary learning method in magnetic flux leakage signal recognition. And compared with the SVM algorithm, the DKSVD algorithm achieves better results.
作者
张陆唯
石玉
刘文波
Zhang Luwei Shi Yu Liu Wenbo(College of Automation Engineering,Nanjing University of Aeronautics and Astronauties,Nanjing 210016 ,China)
出处
《电子测量技术》
2017年第9期193-196,共4页
Electronic Measurement Technology
关键词
钢轨裂纹检测
漏磁信号识别
支持向量机
字典学习
rail flaw detection
magnetic flux leakage signal recognition
support vector machine
dictionary learning
