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Artificial Intelligence Tools for Failure Event Data Management and Probability Risk Analysis for Failure Prevention

Published

Author(s)

Jeffrey T. Fong, Pedro V. Marcal

Abstract

Over the last thirty years, much research has been done on the development of failure event databases and fatigue modeling of crack growth in pressure vessels and piping. According to a USNRC report (NUREG/CR6674, 2000), results of a fatigue crack growth model showed that "cracks initiate rather early in the (nuclear power) plant life. There is about a 50-percent probability of initiating a fatigue crack after only 10 years of operation. Over this 10 years, about 50 percent of these initiated cracks are predicted to grow to become leaking cracks." To improve processing of failure event reporting and more timely risk assessment of critical structures and components, we applied a computer linguistic concept (Schank, 1972) and a natural language toolkit (Lopez, 2002) to develop a software code named ANLAP. This tool will automatically extract statistical data from failure event reports with linkage to fatigue modeling codes for life estimation and risk assessment of aging structures and components.
Proceedings Title
Materials Science & Technology 2009
Conference Dates
October 25-29, 2009
Conference Location
Pittsburgh, PA

Keywords

Aging structures, artificial intelligence, failure event databases, failure prevention, probability risk analysis.

Citation

Fong, J. and Marcal, P. (2009), Artificial Intelligence Tools for Failure Event Data Management and Probability Risk Analysis for Failure Prevention, Materials Science & Technology 2009, Pittsburgh, PA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=903592 (Accessed June 25, 2024)

Issues

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Created October 25, 2009, Updated February 19, 2017