Signal processing for in-line inspection of gas transmission pipelines
Source abstract
Abstract
Abstract Gas transmission pipelines in the United States are primarily inspected using the magnetic flux leakage (MFL) nondestructive evaluation (NDE) technique. However, accurate analysis of the NDE signals in terms of the underlying defects requires a thorough knowledge of various operational parameters such as B-H characteristics of the pipe wall, the velocity of the scanning tool, etc. In certain situations, information about such operational parameters is either absent or hard to obtain. Appropriate signal processing techniques can be applied to the raw MFL signals to ensure that defect characterization is possible in spite of local variations in the test situation. This paper presents two such signal processing methods–one, to compensate the MFL signal for variations in pipe-material grade, and the other to remove the effects of signal distortion that occur due to the velocity of the scanning device.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Geophysical Methods and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (15)
Show source-provided references
1. G. J. Posakony and V. L. Hill.Assuring the Integrity of Natural Gas Transmission Pipelines, Topical Report, GRI 91-0366, Gas Research Institute, Chicago, IL (1992)
2. Y. Shin.Numerical Modeling of Probe-Velocity Effects of Eddy Current NDT, Ph. D. Dissertation, Iowa State University, Ames, IA (1992)
3. R. Palanisamy and W. Lord.IEEE Trans. Mag. MAG-15:1479–1481 (1979)10.1109/TMAG.1979.1060412 ↗
4. E. Barnard and D. Casasent.IEEE Trans. Neural Networks 2:498–508 (1991)10.1109/72.134287 ↗
5. Y. S. Abu-Mostafa and D. Psaltis.IEEE Trans. Pattern Anal. Machine Intell PAMI-7:46–55 (1985)10.1109/TPAMI.1985.4767617 ↗
6. S. S. Udpa and W. Lord.Mat. Eval. 42(9):1136–1141 (1984)
7. C. A. Micchelli.Constr. Approx. 2:11–22 (1986)10.1007/BF01893414 ↗
8. R. C. Gonzalez and P. Wintz.Digital Image Processing, Addison-Wesley, Reading, MA, pp. 246–251 (1987)
9. D. S. Broomhead and D. Lowe.Complex Syst. 2:321–355 (1988)
10. J. T. Tou and R. C. Gonzalez.Pattern Recognition Principles, Addison-Wesley, Reading, MA (1974)
11. M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels.Neural Net. 5:595–603 (1992)10.1016/S0893-6080(05)80038-3 ↗
12. S. Chen, P. M. Grant, and C. F. N. Cowan,IEE Proc. Part F, Radar and Signal Processing, Vol. 139,6:378–384 (1992)10.1049/ip-f-2.1992.0054 ↗
13. R. J. Eiber, T. A. Bubenik, J. B. Nestleroth, S. W. Rust, W. A. Maxley, and D. J. Jones.GRI Nondestructive Evaluation Program: Pipeline Simulation Facility Development, Annual Report, GRI 92/0141, Gas Research Institute, Chicago, IL, p. 91 (1991)
14. D. C. Ghiglia and C. V. Jakowatz.Appl. Optics 24:3830–3837 (1985)10.1364/AO.24.003830 ↗
15. S. Haykin.Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, NJ (1991)