Building Necessary Data Models to Have Accurate AI / ML Outcomes for Integrating AI / ML in Failure Reporting, Analysis, and Corrective Action Systems

Abstract

SUMMARY & CONCLUSIONSIn 2025 [1], we showed the importance of AI/ML implementation in Failure Reporting, Root Cause, and Corrective Action (FRACA) systems was demonstrated. This paper is a continuation of that effort outlining progress and some of the early results of that ongoing effort. It will also delve into potential future applications of AI/ML capabilities within Failure Reporting, Analysis, and Corrective Action (FRACA) systems.Since starting the efforts to implement AI/ML within a FRACA system, baselined output metrics were captured using traditional AI/ML and new technologies for Generative AI. Various data science techniques were implemented to drive accurate results. One of the key issues faced when generating AI/ML systems is data. Another important piece of information is the data that makes up the various aspects of a FRACA System (e.g. data fields, codes, tagging, etc.). AI/ML requires good data to generate successful outcomes. Similarly, FRACA systems are successful when there is a clear framework of required data.By implementing AI/ML on FRACA data, efficiency can be realized by allowing AL/ML to perform repetitive tasks. Integrating AI and ML can simplify error resolution as well as ensure consistency in methodologies for knowledge sharing, best practices, and lessons learned. This paper will focus on optimizing Reliability and Maintainability data that is generated and used in FRACA Systems and AI/ML applications to obtain Safe and Successful outcomes. It will also focus on areas where FRACA systems handle safety related issues and demonstrate improvements when utilizing the best data possible. It will also discuss how to best embed AI/ML into the human process to drive accuracy and confidence. Finally, general recommendations will be provided regarding the collection of FRACA data and how it can enhance AI/ML outcomes.

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