A Study on AI-Driven Predictive Maintenance for Distributed Systems

Abstract

We present a comprehensive framework for AI-driven predictive maintenance, encompassing data collection, preprocessing, feature extraction, model development, and decision-making processes. The paper also discusses the challenges and opportunities in implementing predictive maintenance strategies in distributed environments. Our findings demonstrate significant improvements in system uptime, reduced maintenance costs, and enhanced overall performance through the integration of AI-driven predictive maintenance techniques. The use of artificial intelligence (AI) methods in predictive maintenance for distributed systems is examined in this study. To improve the dependability and effectiveness of intricate distributed systems, the study explores a variety of machine learning algorithms, deep learning models, and data-driven strategies.