Data-Driven Reliability Engineering: The Role of AI in Cloud-Based Predictive Maintenance

Abstract

In the rapidly evolving landscape of cloud computing, ensuring data reliability is paramount for effective predictive maintenance. This paper explores the integration of Artificial Intelligence (AI) in data-driven reliability engineering, specifically within cloud-based environments. We present a comprehensive framework that leverages AI techniques to enhance predictive maintenance capabilities, focusing on fault prediction, anomaly detection, and system reliability optimization. Our approach integrates machine learning algorithms, including supervised learning models (such as Random Forest and Gradient Boosting) and deep learning techniques (including Convolutional Neural Networks and Long Short-Term Memory networks), to analyze historical and real-time data. We demonstrate how these models can predict potential failures, optimize maintenance schedules, and improve overall system reliability. Through empirical analysis and case studies, we illustrate the effectiveness of AI-driven methods in identifying patterns and trends that precede system failures. The results indicate that AI-enhanced predictive maintenance can significantly reduce downtime, extend equipment lifespan, and lower operational costs. Our findings highlight the transformative potential of AI in advancing data-driven reliability engineering and offer insights into best practices for implementing AI-based predictive maintenance solutions in cloudbased infrastructures.