This paper establishes a dynamic Bayesian network to model the growth of corrosion defects on energy pipelines. The integrated model characterizes the growth of defect depth by a homogeneous gamma process and considers the biases and random errors associated with the in-line inspection (ILI) tools. The distributions of the mean value and coefficient of variation of the annual growth of defect depth are learned from multiple ILI data using the parameter learning technique of Bayesian networks. With the same technique, the distributions of the biases and standard deviation of random errors associated with ILI tools are learned from ILI data and their corresponding field measurements. An example with real corrosion management data is used to illustrate the process of developing the model structure, learning model parameters and predicting the corrosion growth and time-dependent failure probability. The results indicate that the model can in general predict the growth of corrosion defects with reasonable accuracy and the ILI-reported and field-measured depth can be used to update the time-dependent failure probability in a near-real-time manner. In comparison with existing growth models, the graphical feature of Bayesian networks makes it more intuitive and transparent to users. The employment of parameter learning provides a semi-automated and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages will facilitate the application of the model in the practice of corrosion management in pipeline industry.
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2018 12th International Pipeline Conference
September 24–28, 2018
Calgary, Alberta, Canada
Conference Sponsors:
- Pipeline Division
ISBN:
978-0-7918-5186-9
PROCEEDINGS PAPER
Corrosion Growth Modeling by Learning a Dynamic Bayesian Network From Multiple In-Line Inspection Data Available to Purchase
Wei Xiang,
Wei Xiang
Western University, London, ON, Canada
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Wenxing Zhou
Wenxing Zhou
Western University, London, ON, Canada
Search for other works by this author on:
Wei Xiang
Western University, London, ON, Canada
Wenxing Zhou
Western University, London, ON, Canada
Paper No:
IPC2018-78118, V001T03A026; 11 pages
Published Online:
November 6, 2018
Citation
Xiang, W, & Zhou, W. "Corrosion Growth Modeling by Learning a Dynamic Bayesian Network From Multiple In-Line Inspection Data." Proceedings of the 2018 12th International Pipeline Conference. Volume 1: Pipeline and Facilities Integrity. Calgary, Alberta, Canada. September 24–28, 2018. V001T03A026. ASME. https://doi.org/10.1115/IPC2018-78118
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