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Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines
Author(s) -
Sinha Sunil K.,
Pandey Mahesh D.
Publication year - 2002
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/1467-8667.00279
Subject(s) - pipeline transport , artificial neural network , probabilistic logic , reliability (semiconductor) , magnetic flux leakage , fuzzy logic , probabilistic neural network , engineering , reliability engineering , computer science , machine learning , artificial intelligence , time delay neural network , power (physics) , physics , quantum mechanics , environmental engineering , mechanical engineering , magnet
A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation‐based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation‐based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.