Intellectual Property India Publishes Patent Application for 'Iot-Based Non-Invasive Monitoring System For Real-Time Performance Analysis And Ai-Driven Predictive Maintenance Of Split Air Conditioning Units' Filed by Nitte Meenakshi Institute Of Technology, Nitte; Ms. Anagha K K; Ms. Hemalatha P; Mr. Maaz Jauhar; Dr. Parameshachari Bidare Divakarachari; and Dr. Kapilan N

MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641051250 A) filed by Nitte Meenakshi Institute Of Technology, Nitte; Ms. Anagha K K; Ms. Hemalatha P; Mr. Maaz Jauhar; Dr. Parameshachari Bidare Divakarachari; and Dr. Kapilan N, Bangalore, Karnataka, on April 22, for 'iot-based non-invasive monitoring system for real-time performance analysis and ai-driven predictive maintenance of split air conditioning units.'

Inventor(s) include Nitte Meenakshi Institute Technology, Nitte; Ms. Anagha K K; Ms. Hemalatha P; Mr. Maaz Jauhar; Dr. Parameshachari Bidare Divakarachari; and Dr. Kapilan N.

The application for the patent was published on May 1, under issue no. 18/2026.

According to the abstract released by the Intellectual Property India: "The present invention relates to an IoT-based non-invasive monitoring system for real-time performance analysis and AI-driven predictive maintenance of split air conditioning units. The system comprises a Non-Invasive Sensor Module with clip-on temperature sensors, pressure sensors, power meter, and vibration sensors attachable to existing AC units without modification; an Edge Computing Unit with local processor for data preprocessing and COP/EER efficiency calculation; a Cloud Platform with server, analytics engine, and visualization dashboard accessible through web and mobile interfaces; and an AI/ML Module with anomaly detection, predictive maintenance using Random Forest and MLP Neural Networks, and fault diagnosis capabilities. The system addresses the need for cost-effective retrofit monitoring of legacy split AC systems by providing comprehensive performance analysis without requiring invasive modifications that would void warranties. Multi-parameter sensing captures temperature, pressure, power, and vibration data enabling true COP/EER-oriented diagnostics. Edge computing reduces bandwidth and enables real-time efficiency estimation. Machine learning algorithms trained specifically on split AC patterns detect anomalies, predict failures, and diagnose root causes including refrigerant leakage, compressor stress, and airflow obstruction. The consumer-friendly solution enables individual split AC owners to reduce energy consumption, prevent unexpected breakdowns, optimize maintenance timing, and lower carbon emissions through data-driven insights and actionable recommendations."

Disclaimer: Curated by HT Syndication.