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02073 RISK MINING: A PREDICTIVE TOOL TO ENHANCE PIPELINE INTEGRITY ASSESSMENT

Picture for 02073 RISK MINING: A PREDICTIVE TOOL TO ENHANCE...
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Product Number: 51300-02073-SG
ISBN: 02073 2002 CP
Author: Stephen F. Biagiotti, Jr, Mark Madden, and Elaine S. Hendren
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Standard methods of evaluating pipeline integrity have stressed index-based and conditional based data assessment processes. Recent works, however, have emphasized the importance of predictive techniques using associations, correlations, sequential patterns and other relationships in evaluating pipeline integrity. Data mining represents a shift from verification-driven data analysis approaches to discovery-driven methods in integrity evaluation. Risk mining involves the analysis of large quantities of data in the process of discovering meaningful new correlations, patterns and trends using pattern recognition technologies as well as statistical and mathematical techniques. This paper will focus on the use of data mining related methods in analyzing pipeline/engineering data, identifying correlations, development of industry-based algorithms and in the determination of relationships that influence root cause and consequence of failure in pipeline. The cost benefits of using such a model in pipeline risk assessment are highlighted.
Standard methods of evaluating pipeline integrity have stressed index-based and conditional based data assessment processes. Recent works, however, have emphasized the importance of predictive techniques using associations, correlations, sequential patterns and other relationships in evaluating pipeline integrity. Data mining represents a shift from verification-driven data analysis approaches to discovery-driven methods in integrity evaluation. Risk mining involves the analysis of large quantities of data in the process of discovering meaningful new correlations, patterns and trends using pattern recognition technologies as well as statistical and mathematical techniques. This paper will focus on the use of data mining related methods in analyzing pipeline/engineering data, identifying correlations, development of industry-based algorithms and in the determination of relationships that influence root cause and consequence of failure in pipeline. The cost benefits of using such a model in pipeline risk assessment are highlighted.
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