Abstract
In order to meet the engineering requirement of gas pipeline inline inspection for cracking defects, a new ultrasonic guided wave (GW) method based on electromagnetic acoustic transducer (EMAT) has emerged. In consideration of the large amount of original inspection data and the limited capacity of the storing device, the research on rapid and large ratio data compression is needed. This paper proposes a new large ratio data compression method based on subsection adaptive method (SAM) and wavelet neural network (WNN) algorithm. The influences of parameter selection on the compression quality, speed and ratio and the method of enhancing the compression speed of this method is studied. The experiment results indicate that compared with the traditional WWN compression algorithm, the proposed method has higher compression quality and speed, and a large compression ratio of 110:1 is achieved by the proposed method. It is thus suitable for gas pipeline crack inline inspection based on EMAT-generated GW.