In order to decrease the false alarm rate and improve the sensitivity of pipeline fault diagnosis system, three artificial intelligence based methods are first proposed. Neural networks with the input matrix composed by stress wave characteristics in time domain or frequency domain is proposed to classify various situations of the pipeline, in order to detect the leakage from pipeline online running data. Context-free grammar of symbolic representation of the negative wave form is used and a negative wave form parsing system with application to syntactic pattern recognition based on the representation is described. New complex thermal and hydraulic models, in which the flow regime, viscosity-temperature characteristics, density-temperature characteristics and specific heat-temperature characteristics, etc., of the running fluid in the pipelines are set up for non-isothermal pipeline carrying higher temperature fluid or in ambient environment.
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1998 2nd International Pipeline Conference
June 7–11, 1998
Calgary, Alberta, Canada
Conference Sponsors:
- Pipeline Division
ISBN:
978-0-7918-4023-8
PROCEEDINGS PAPER
Pipeline Fault Diagnosis System Enhancement
Tang Xiujia
Tang Xiujia
Peking University, China
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Tang Xiujia
Peking University, China
Paper No:
IPC1998-2097, pp. 835-841; 7 pages
Published Online:
October 21, 2016
Citation
Xiujia, T. "Pipeline Fault Diagnosis System Enhancement." Proceedings of the 1998 2nd International Pipeline Conference. Volume 2: Design and Construction; Pipeline Automation and Measurement; Environmental Issues; Rotating Equipment Technology. Calgary, Alberta, Canada. June 7–11, 1998. pp. 835-841. ASME. https://doi.org/10.1115/IPC1998-2097
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