Abstract:

The method wavelet energy coefficients combined with BP neural network is used to distinguish pipeline grooves from welds.A guided wave detection system was established,a set of test samples of pipeline grooves from welds were collected,and wavelet energy coefficients were extracted from test samples and applied to the training and recognition of BP neural network.Results show that the identification accuracy of pipeline defects of this method is high and stable with a minimum identification accuracy of 92% and 98% for weld and groove respectively,and a highest recognition accuracy of 100% in the 5 experiments on randomly chosen samples.

Key words: ultrasonic guided wave;wavelet energy coefficient;neural network;pipeline;defect identification