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Algorithm Study for Metal Magnetic Memory Signal Processing

[+] Author Affiliations
Zhanjun Feng, Weibin Wang, Song Lin

PetroChina Pipeline R&D Center, Langfang, Hebei, China

Hongjun Dong, Dianxue Wang

PetroChina Pipeline Company, Langfang, Hebei, China

Kai Wu

PetroChina Pipeline Company, Lanzhou, China

Paper No. IPC2012-90033, pp. 677-680; 4 pages
  • 2012 9th International Pipeline Conference
  • Volume 1: Upstream Pipelines; Project Management; Design and Construction; Environment; Facilities Integrity Management; Operations and Maintenance; Pipeline Automation and Measurement
  • Calgary, Alberta, Canada, September 24–28, 2012
  • Conference Sponsors: International Petroleum Technology Institute, Pipeline Division
  • ISBN: 978-0-7918-4512-7
  • Copyright © 2012 by ASME


Currently, Metal Magnetic Memory (MMM) non-destructive testing has been applied on trenchless inspection for buried pipeline. However, the problem of signal pattern recognition still exists. This paper introduces two signal processing methods to enhance the inspection accuracy of MMM: the signal segmented fluctuation and the signal segmented dissimilarity. Both of the two methods are designed for Signal singularity detection (SSD) of MMM signal. It is well known that the MMM signal of the stress concentrated area (SCA) is more fluctuant and dissimilar in contrast to those of non SCA, which is the basis of deriving the two algorithms. The two methods have the advantage in the detection of the metallic work piece which is non defective in looks but fatigued as well as may be helpful to detect the singularity of the signals such as negative pressure wave, ultrasonic wave etc. Experimental result with real data demonstrates the effectiveness of the proposed algorithms. Moreover, the MMM SSD software implementation is considered.

Copyright © 2012 by ASME



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