NIST logo

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

NIST Authors in Bold

Author(s): Jeffrey T. Fong; Pedro V. Marcal;
Title: Artificial Intelligence Tools for Failure Event Data Management and Probability Risk Analysis for Failure Prevention
Published: October 25, 2009
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: Materials Science & Technology 2009
Location: Pittsburgh, PA
Dates: October 25-29, 2009
Keywords: Aging structures; artificial intelligence; failure event databases; failure prevention; probability risk analysis.
Research Areas: Modeling, Software, Statistics, Scientific Computing, Information Technology, Materials Science, Math
PDF version: PDF Document Click here to retrieve PDF version of paper (1MB)