MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541122483 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Engineering College For Women; Malla Reddy College Of Engineering And Technology; Malla Reddy Vishwavidyapeeth; and Malla Reddy University, Medchal-Malkajgiri, Telangana, on Dec. 5, 2025, for 'predictive maintenance model for data center operations.'
Inventor(s) include Dr Ramu Vankudoth; Dr. Pradeep Venuthurumilli; N Ramesh; Dr. Ramana Hechhu; and Dr. Arun Singh Chouhan.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "A predictive maintenance model is aimed at maximizing the reliability and operational efficiency of the data center infrastructure by using real-time analytics and predicting the fault-based failures. The inference is an anomaly detection algorithm along with sensor-based monitoring framework along with machine learning algorithms to forecast the most likely equipment failure, before they occur. It summarizes information in a variety of sources such as cooling units, power units, live sync, tables, network unit engines and transforms raw telemetry data into information about maintenance action. The architecture makes use of a multi-layer approach which includes data acquisition, preprocessing, predictive modelling and decision automation. Based on the historical data of the performance, machine learning should be trained to perform a detailed evaluation of the temperature, vibration, power consumption and airflow indicators to determine if it is degrading beforehand. Contextual reasoning layer matches these anomalies to the patterns of operation and this allows the system to draw the difference between transient and real failure signals. An admission alert and scheduling module has a prioritization that is according to criticality, expense, and interdependency between components. The system will decrease unplanned downtimes, energy wastage, and wear on equipment because it will recommend interventions only when necessary, as opposed to scheduled intervals. Whereas experimental validation in simulated conditions in data centers is used, it is shown that it enhances uptime, optimized cooling efficiency and increased service continuity. The framework will facilitate proactive management of assets and scalable deployment in multi-vendor data center setting to ensure predictive intelligence will be an asset of the updated infrastructure functions."
Disclaimer: Curated by HT Syndication.
