Leveraging Inspection Database Management System to Bridge between Data Analytics and Big Data

Inspection reduces uncertainty by identifying the true damage state. Inspection only sample the overall area of a plant which may lead to inaccurate and unrealistic outcome. In this study, big data is generated with the use of an advanced technique to collect millions of readings coupled with visual images for various forms of degradations. The data is then overplayed on realistic models to be processed by an IDMS with embedded statistical methods to improve risk and reliability analysis for better predictions of future probability of failure. This work provides an overview over this advanced integrity management approach using the big data. Through this integration of big data, IDMS and customized engineering analysis, users will be able to reduce the risk while maximizing asset performance.