Intellectual Property India Publishes Patent Application for Ai-Assisted Predictive Maintenance System For Renewable Energy Plants

MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088104 A) filed by Dr. A. Sagai Francis Britto; Mr. Vinod L.; Ms. Gagana B. R.; Dayananda Sagar Academy Of Technology And Management; Dr. Vinay Gangaraju; Mrs. Bala Veeravatnam; Prof. Vishal Gotarane; Mr. Sukruth Sagar B. P.; Dr. Aditya Prasad Padhy; and Dr. Amitava Biswas on July 19, 2026, for Ai-Assisted Predictive Maintenance System For Renewable Energy Plants.

Inventors include Dr. A. Sagai Francis Britto; Mr. Vinod L.; Ms. Gagana B. R.; Dr. Vinay Gangaraju; Mrs. Bala Veeravatnam; Prof. Vishal Gotarane; Mr. Sukruth Sagar B. P.; Dr. Aditya Prasad Padhy; and Dr. Amitava Biswas.

The application for the patent was published on July 24, 2026, under issue no. 30/2026.

Abstract: [051] The present invention discloses an AI-assisted predictive maintenance system for renewable energy plants. The system includes a plant asset interface, sensor acquisition module, edge preprocessing module, sensor fusion and environmental normalisation module, AI-based degradation prediction engine, digital twin comparison module, remaining useful life estimation module, confidence validation module, adaptive maintenance scheduling module, and feedback learning module. The system receives electrical, thermal, mechanical, vibration, acoustic, environmental, and maintenance history data from renewable energy plant assets such as solar photovoltaic panels, inverters, wind turbines, gearboxes, bearings, converters, transformers, and battery units. The acquired data is filtered, time-aligned, normalised according to environmental and load conditions, and analysed to determine component-specific degradation fingerprints. The digital twin comparison module compares expected asset behaviour with actual asset behaviour to validate probable degradation. The system estimates remaining useful life and generates preventive maintenance actions before failure, thereby reducing downtime and improving operational reliability of renewable energy plants. Accompanied Drawing [FIGS. 1-2]

Disclaimer: Curated by HT Syndication.