Artificial Intelligence (AI) Tools for Data Acquisition and Probability Risk Analysis of Nuclear Piping Failure Databases

Over the last thirty years, much research has been done on the development and application of in-service inspection (ISI) and failure event databases for pressure vessels and piping, as reported in two recent symposia: (1) ASME 2007 PVP Symposium (in honor of the late Dr. Spencer Bush), San Antonio, Texas, on “Engineering Safety, Applied Mechanics, and Nondestructive Evaluation (NDE).” (2) ASME 2008 PVP Symposium, Chicago, Illinois, on “Failure Prevention via Robust Design and Continuous NDE Monitoring.” The two symposia concluded that those databases, if properly documented and maintained on a worldwide basis, could hold the key to the continued safe and profitable operation of numerous aging nuclear power or petro-chemical processing plants. During the 2008 symposium, four uncertainty categories associated with causing uncertainty in fatigue life estimates were identified, namely, (1) Uncertainty-1 in failure event databases, (2) Uncertainty-2 in NDE databases, (3) Uncertainty-3 in material property databases, and (4) Uncertainty-M in crack-growth and damage modeling. In this paper, which is one of a series of four to address all those four uncertainty categories, we introduce an automatic natural language abstracting and processing (ANLAP) tool to address Uncertainty-1. Three examples are presented and discussed.