Smart Failure Anticipation in Modern Data Centers through AI-Enabled Maintenance Analytics

DOI

10.2991/978-94-6239-678-4_29How to use a DOI?

Keywords

Artificial Intelligence; Predictive Maintenance; Data Centre Management; Machine Learning; Energy Efficiency; Sustainability; Fault Detection; Green Computing

Abstract

The transformation of the data center is progressively controlled by the predictive maintenance methods of Artificial Intelligence (AI) that ensure sustainability, efficiency, and reliability. Old-fashioned reactive and schedule-based maintenance plans cannot be applied in the complex infrastructures and produce unneeded costs in operation, downtime of the system, and environmental pressure. This paper examines the ability of AI and Machine Learning (ML) technologies to transform the process of data centers management with precision in fault prediction, real-time optimization, and self-healing systems. The sensor networks are up-to-date and monitor temperature, voltage, vibration, and energy consumption, which is analyzed in real-time to be smart. AI programs such as neural networks, decision trees, and support vectors work on the data to forecast hardware failure and undertake proactive maintenance measures in the form of component replacement, performance optimization and automated software patches. The research has the descriptive-analytical methodology and summarizes the findings of the studies carried out between 2020 and 2025. It has been found that AI predictive maintenance will lower hardware failures by 30–50 percent, conserve up to 40 percent of energy, and increase equipment life. In addition, it increases green sustainability through reduction of green power and e-waste. According to the research, the outcome of human capacity coupled with AI intelligence is powerful, energy-efficient, and green data centers.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Booma Jayapalan
AU  - A. Gokul
AU  - M. Anish Raj
PY  - 2026
DA  - 2026/05/28
TI  - Smart Failure Anticipation in Modern Data Centers through AI-Enabled Maintenance Analytics
BT  - Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
PB  - Atlantis Press
SP  - 365
EP  - 376
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-678-4_29
DO  - 10.2991/978-94-6239-678-4_29
ID  - Jayapalan2026
ER  -