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Neuro-Fuzzy Approaches for FRP Oil and Gas Pipeline Condition Assessment

[+] Author Affiliations
S. Kumar, F. Taheri

Dalhousie University, Halifax, NS, Canada

Paper No. PVP2004-3080, pp. 271-275; 5 pages
doi:10.1115/PVP2004-3080
From:
  • ASME/JSME 2004 Pressure Vessels and Piping Conference
  • Storage Tank Integrity and Materials Evaluation
  • San Diego, California, USA, July 25–29, 2004
  • Conference Sponsors: Pressure Vessels and Piping Division
  • ISBN: 0-7918-4685-7
  • Copyright © 2004 by ASME

abstract

Recent advances in ultrasonic, optical and piezoelectric sensors, and computing technologies have led to the development of inspection systems for underground and off-shore structures such as water lines, oil and gas pipes, and telecommunication conduits. It is now possible to use inspection technologies that require no human intervention (i.e., having had to go underground or off-shore); moreover, the inspection process can be fully automated, from data acquisition to data analysis, and eventually to condition assessment and repair. This paper describes the development of an automated data interpretation system for fiber-reinforced polymer composites (FRP) oil and gas pipelines, which would also be applicable to metallic pipes. The interpretation system obtains C-scan image data from so-called “smart pigs” and maps data using Geographic Information System (GIS) and Global Positioning System (GPS). Assessment of health of pipelines using neural networks is then performed to identify the high-risk locations in each pipeline or pipeline network, thus allowing the inspection to be properly targeted. The proposed system utilizes artificial neural networks and genetic algorithm to recognize various types of defects in FRP oil and gas pipelines. Image processing and wavelets techniques are used to find the detail of the damage geometry. An expert system is also developed, using fuzzy Logic, to perform damage condition assessment and suggest an optimum repair protocol. The framework of the developed system, thus includes GIS, risk map, modification of digital images for preprocessing, image feature segmentation, utilization of multiple neural networks for feature pattern recognition, the fusion of multiple neural networks via the use of fuzzy logic systems, and the proposed expert system for suggested repair.

Copyright © 2004 by ASME

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