Affiliations: [a] State Key Lab of Power Systems, Department of
Electrical Engineering, Tsinghua University, Beijing, China | Université Laval, Canada | Tohoku University, Japan
Correspondence:
[*]
Corresponding author: Songling Huang, State Key Lab of Power
Systems, Department of Electrical Engineering, Tsinghua University, Beijing
100084, China. Tel./Fax: +86 10 6277 2131; E-mail:
[email protected]
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.
Keywords: EMAT-generated GW inspection, large ratio data compression, subsection adaptive method, WNN algorithm
