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Anticipated Results:
The proposed flaw inspection approach is novel by itself and is right on the target for the underground pipeline Transporation infrastructure. There are about 160 pipeline companies in the United States, operating over 285,000 miles of pipe. The proposed system combines the latest development in sensor technology with most recent theories in signal processing to detect mechanical failures and hence will provide valuable information that could save costs for the crude, petroleum and natural gas transportation industry. It will also help to make pipeline structures safe. The system has low computation burden and hence is suitable for real-time applications. Automatic tools for dignosis will also be very useful for other structural integrity approaches, We expect the market for such a system to be in the 50 million range.
Intelligent Automation, Inc. (IAI) and Oak Ridge National Lab (ORNL) is developing a novel and integrated approach to inspect mechanical damages in the pipelines with or without coatings. It extends our successful work in a previous phase I project that combines the state-of-the-art Shear Horizontal (SH) wave EMAT technique, through detailed numerical modeling and instrumentation ofr data collection, with advanced signal processing and pattern classification techniques, to detect and characterize the mechanical dents in the underground pipeline transportation infrastructures. In particular, this work will address several challenging issues: (1) Through guided wave modal analysis through three-dimensional (3-D) Boundary Element Method (BEM) for best wave mode and frequency selection and defect volumetric features extraction, (2) Circumferential and axial ultrasonic SH wave EMATs and fixture design for data collection and analysis, (3) Advanced signal processing algorithms such as Principal Component Analysis (PCA) and discriminate Analysis (DA) for mechanical defect signature extraction and pattern classification, and (4) small dents and coating condition assessment. The envisioned system will be mounted on a PIG for accurately and reliably detecting the metal loss and other defects in the pipeline infrastructures long ahead before they need remediation.
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