Acoustic Emission Signal Classification based on Support Vector Machine
نویسندگان
چکیده
Acoustic emission method has a major application in the detection of the oil storage tank damage. Therefore, classification of acoustic emission signals has great significance. A classification method based on support vector machines is proposed for its good generalization performance and less training data. Based on cross validation method, the genetic algorithm is compared with the grid search algorithm. The best parameters of the RBF kernel had obtained by using grid optimization method, and the classifier had built to achieve the identification and classification of acoustic emission signals. The simulation results show that support vector machine can effectively distinguish different acoustic emission signal and noise signal.
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