AI-Driven Data Center Infrastructure, Mechanical Piping Systems, and Reliability Engineering: Energy Optimization, Fault Prediction
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P119Keywords:
Data Center Infrastructure, Mechanical Piping System, Artificial Intelligence, Energy Optimization, Decision-Making Models, Fault DiagnosisAbstract
The fast development of artificial intelligence and high-density computing has dramatically enhanced the energy use and operational complexity of the current data centers and rendered reliability and sustainability design a thorough evaluation of AI-based data center infrastructure, with a specific emphasis on mechanical piping systems, energy conservation, and reliability engineering. Mechanical piping networks are critical to thermal control, as they regulate coolant circulation, heat transfer, and system stability. The paper discusses the application of artificial intelligence and machine learning methods to make piping and cooling systems smarter in terms of monitoring, predictive maintenance, and fault detection via real-time sensor data. The advanced AI-driven models aid in detecting anomalies, the remaining useful life, and root cause analysis, minimizing unplanned downtime and enhancing the strength of the system. Also, the paper examines AI-based energy optimization methods, such as adaptive cooling, intelligent flow regulation, and digital twin integration, which lead to considerable improvements in power consumption and cost. such as data integration, model explain ability, scalability, and real-time deployment are distinguished. Lastly, promote sustainable reliable and energy-efficient data center infrastructure using smart automation and artificial intelligence decision making.
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