%0 Journal Article
%T 基于支持向量机的油管内外表面缺陷识别方法<br>Identification of Oil Tube Defects on Internal and External Surfaces Based on SVM
%A 蹇清平
%A 艾志久
%A 张勇
%A 杨赟达
%J 机械科学与技术
%D 2015
%X 由于根据漏磁信号难以准确识别出油管内、外表面缺陷，为此提出了基于支持向量机（SVM）的油管内外表面缺陷识别方法。采用时频分析技术提取了用于区分油管内外表面缺陷的漏磁信号时域和频域特征量，然后将其作为油管内外表面缺陷识别SVM模型的样本数据，采用改进的云自适应粒子群（MACPSO）优化算法对SVM识别模型的参数进行优选，结合优选的模型参数和样本数据训练构建油管内外表面缺陷识别SVM模型。实验结果表明：该智能识别方法能够有效区分油管的内外表面缺陷，识别准确率高于90%。<br>There are some difficulties to accurately identify the defects on the internal and external surfaces of the oil tube by the magnetic flux leakage(MFL) signal. Hence, this paper presents an identification method to achieve it based on support vector machines (SVM). First, the time-frequency analysis approach is employed to acquire the feature vectors of the time domain and frequency domain from MFL signal. Then, the feature vectors are used as the sample data of the proposed model of defect identification based on SVM. The parameters of this model are optimized by the Modified Adaptive Particle Swarm Optimization(MCAPSO)algorithm based on cloud theory. Finally, with the optimized model parameters and training sample data, the identification model of defect on internal and external surfaces based on SVM was established. The experimental results show that the proposed approach is effective on the identification of the oil tube defects between the internal and external surfaces, and its accuracy is higher than 90%
%K 油管
%K 支持向量机
%K 内外表面缺陷
%K 粒子群优化
%K 漏磁信号&lt
%K br&gt
%K feature extraction
%K frequency domain analysis
%K internal and external surface defects
%K magnetic flux leakage
%K oil tube
%K particle swarm optimization(PSO)
%K support vector machines
%K time domain analysis
%U http://journals.nwpu.edu.cn/jxkxyjs/CN/abstract/abstract5924.shtml