An ultrasonic flaw-classification system with wavelet-packet decomposition, a mutative scale chaotic genetic algorithm, and a support vector machine and its application to petroleum-transporting pipelines

In this paper, a novel system for ultrasonic flaw classification is proposed, which is based on wavelet-packet decomposition (WPD), a support vector machine (SVM), and a new chaotic optimization algorithm (mutative scale chaotic genetic algorithm, MSCGA). In this system, WPD is employed to extract the features of ultrasonic flaw signals, an SVM classifier is used to classify the flaws, and an MSCGA is employed as a feature selector to get rid of redundant and irrelevant features. In an experiment, a petroleum-transporting pipeline sample with various types of flaws is analyzed with this system. Experimental results show that the proposed system can improve the performance of the SVM during classification of the flaws in the petroleum-transporting pipeline. For comparison, we test the system without any feature selectors and the system with different feature selectors, respectively. The results show that the novel system is powerful and effective for ultrasonic flaw classification.

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