Author： Ji Fengzhu Wang Changlong Zuo Xianzhang Hou Songshan Liang Siyang
Publisher： The British Institute of Non-Destructive Testing
ISSN： 1354-2575
Source： Insight - Non-Destructive Testing and Condition Monitoring, Vol.49, Iss.9, 2007-09, pp. : 516-520
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Abstract
Magnetic flux leakage techniques are used extensively to detect and characterise defects in natural gas and oil transmission pipelines. Based on the least squares support vector machines (LS-SVMs) technique, this paper presents a novel approach for the three-dimensional (3-D) defect profile reconstructed from magnetic flux leakage signals. The basic theory of LS-SVM for function estimates is given. The hyper-parameters of the LS-SVMs problem formulations are tuned using a 10-fold cross validation procedure and a grid search mechanism, and applying the pruning algorithm to impose sparseness on the LS-SVMs. The training data are composed of the measured and simulated data. A mapping from MFL signals to 3-D profiles of defects is established, the reconstruction of 3-D profiles of defects from magnetic flux leakage inspection signals is achieved and 3-D error of reconstruction results is analysed. The experimental results show that the LS-SVM has high precision, good generalisation ability and capability of tolerating noise.
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