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<p>This study proposes a systematic framework for predictive maintenance and anomaly detection in data center Evaporative Cooling Systems (ECS), leveraging supervised learning models to forecast operational parameters and identify potential deviations from normal conditions. In both hyperscale and non-hyperscale data centers, thermal management plays a critical role in addressing energy inefficiencies and reducing the risk of system failures. The objective of this study is to predict fan speeds, airflow, energy consumption, and cold aisle temperatures to enhance system reliability and operational efficiency. A multi-stage predictive approach is developed, incorporating advanced data preprocessing, feature engineering, and machine learning models. In the first stage, Sequential Neural Networks (SNN) predict fan speeds with high accuracy, achieving a mean absolute error (MAE) of 0.45, root mean squared error (RMSE) of 0.59, and an R² score of 0.98, outperforming the baseline Long Short-Term Memory (LSTM) model. These predictions serve as inputs to a second-stage Random Forest (RF) model, which forecasts airflow and thermal parameters with R² scores of 0.965 and 0.99, respectively. Anomalies are flagged when deviations exceed predefined thresholds and persist beyond a critical duration, triggering real-time alerts for timely intervention. These results validate the effectiveness and robustness of the SNN model in enabling early anomaly detection and providing actionable insights for proactive thermal management. Future work will explore dynamic fan speed optimization to further reduce temperature variance and energy consumption, thereby improving the sustainability of data center cooling systems.</p>

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Multi-stage predictive framework for early anomaly detection and real-time alerts in data center thermal systems

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Rushil Kaushikkumar Patel,
  • Yuxin Yang,
  • Rohan Kulkarni,
  • Sang Won Yoon

摘要

This study proposes a systematic framework for predictive maintenance and anomaly detection in data center Evaporative Cooling Systems (ECS), leveraging supervised learning models to forecast operational parameters and identify potential deviations from normal conditions. In both hyperscale and non-hyperscale data centers, thermal management plays a critical role in addressing energy inefficiencies and reducing the risk of system failures. The objective of this study is to predict fan speeds, airflow, energy consumption, and cold aisle temperatures to enhance system reliability and operational efficiency. A multi-stage predictive approach is developed, incorporating advanced data preprocessing, feature engineering, and machine learning models. In the first stage, Sequential Neural Networks (SNN) predict fan speeds with high accuracy, achieving a mean absolute error (MAE) of 0.45, root mean squared error (RMSE) of 0.59, and an R² score of 0.98, outperforming the baseline Long Short-Term Memory (LSTM) model. These predictions serve as inputs to a second-stage Random Forest (RF) model, which forecasts airflow and thermal parameters with R² scores of 0.965 and 0.99, respectively. Anomalies are flagged when deviations exceed predefined thresholds and persist beyond a critical duration, triggering real-time alerts for timely intervention. These results validate the effectiveness and robustness of the SNN model in enabling early anomaly detection and providing actionable insights for proactive thermal management. Future work will explore dynamic fan speed optimization to further reduce temperature variance and energy consumption, thereby improving the sustainability of data center cooling systems.