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Research of the Petroleum Pipelines Corrosion Based on Bayesian Networks

Author: RenHua
Tutor: YeYing
School: Huazhong University of Science and Technology
Course: Probability Theory and Mathematical Statistics
Keywords: Bayesian network artificial intelligence directed acyclic graph Structure learning parameter learning K2 algorithm
CLC: TE988.2
Type: Master's thesis
Year: 2009
Downloads: 50
Quote: 0
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Abstract


The problem of uncertainty is the focus of research in artificial intelligence. People pay more and more attention to uncertainty reasoning and decision. Bayesian network is a marriage between probability theory and graph theory. It’s natural compact graphical representation of joint Probability distribution. BNs has many advantages such as solid statistics grounding,explicit semantic structure,flexible reasoning ability, convenient decision making mechanism, and efficient learning mechanism. So it’s becoming one of best methods to deal with uncertainty problem. The key point in Bayesian network theory is construction. One of effective construction methods is studying from data. Bayesian Network learning mainly includes structure learning and Parameter learning. Parameter learning learns parameters from data sets given net structure. It’s not very difficult. But it’s difficult to learn net structure from data sets, and the cardinality of search space increases to the number of variables exponently. It’s proved that the network structure learning is NP-hard.Many different structure learning algorithms have been proposed recently. K2 algorithm is one of efficient and accurate algorithms. Concerned that petroleum industry is facing the serious Corrosion problem in China, the paper takes the corrosion on the petroleum pipelines as the example, learns the Bayesian network structure of the petroleum pipelines corrosion problem by K2 algorithm, takes inferences by combining prior knowledge and the learned structure, and concludes the main reasons that the petroleum pipelines corrode.

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CLC: > Industrial Technology > Oil and gas industry > Oil machinery and equipment and automation > Corrosion and Protection of machinery and equipment > Corrosion and Protection of Oil and Gas Storage and Transportation Equipment > Pipeline corrosion and protection
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