The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of component failures. The implementation framework encompasses system integration, data management, model development, and operational integration, leading to substantial improvements in maintenance efficiency, cost reduction, and equipment longevity. The convergence of human expertise with AI capabilities marks a significant advancement in predictive maintenance, revolutionizing how organizations approach data center operations and reliability management.
Keywords: Artificial Intelligence, Predictive Maintenance, edge computing, machine learning, sensor networks
