Abstract
This paper is concerned with the detection of physical flaws on pipe walls in gas pipelines. The sensor technology is EMAT (electromagnetic acoustic transducer), a non-contact ultrasonic technology. One EMAT is used as a transmitter, exciting an ultrasonic impulse into the pipe wall. Another EMAT located a few inches away from the first is used as a receiving transducer. This paper reports on the identification of flaw signatures in the receiver output. The first step in flaw characterization is to perform wavelet analysis of the signature. Being non-shift-invariant, an array of coefficients of a discrete wavelet transform of a signal is not directly suitable as a pattern recognition feature. However, comparing composite properties of the signal on different scales is useful, because the mode conversion caused by a flaw, changes the composite properties of the signal in wavelet space. For EMAT data, the useful information projects onto five mutually orthogonal wavelet scales. This paper reports on the use of a robust 17-dimensional feature vector that consistently distinguishes "flaw" signatures from "no-flaw" signatures in a substantial collection of experimental data.
Original language | English |
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Pages (from-to) | 217-226 |
Number of pages | 10 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5103 |
DOIs | |
State | Published - 2003 |
Event | PROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: Intelligent Computing: Theory and Applications - Orlando, FL, United States Duration: Apr 21 2003 → Apr 22 2003 |
Keywords
- Data compression
- EMAT
- Feature vector
- Flaw detection
- Mahalanobis distance
- Pattern recognition
- Pipeline
- Process monitoring
- Real-time processing
- Wavelet