Paper Contents
Abstract
Failure Root Cause Analysis (FRCA) is a critical process in identifying and addressing the underlying factors behind system or component failures. Traditional methods, often manual and time-intensive, can miss subtle patterns that contribute to these failures. This paper explores the integration of Artificial Intelligence (AI) in automating and enhancing FRCA, offering innovative techniques that accelerate and improve the accuracy of failure detection and diagnosis. By leveraging machine learning algorithms, data analytics, and anomaly detection, AI can process vast datasets, identifying patterns and correlations that are not readily visible through conventional approaches. These advanced AI-based methodologies not only increase the precision of root cause identification but also provide predictive capabilities, enabling proactive measures to prevent failures before they occur. Furthermore, the study discusses how AI-driven systems can adapt and evolve with new data inputs, continuously refining their analytical models to improve reliability and operational efficiency. The implementation of AI in FRCA presents a transformative shift in industries where high-reliability systems are paramount, reducing downtime and enhancing overall system longevity
Copyright
Copyright © 2023 Akshay Gaikwad, Fnu Antara, Krishna Gangu, Raghav Agarwal, Shalu Jain, Prof. Dr Sangeet Vashishtha . This is an open access article distributed under the Creative Commons Attribution License.
