MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641076512 A) filed by Muthayammal Engineering College Autonomous on June 20, 2026, for Cognitive Asset Surveillance System For Real-Time Equipment Degradation Detection And Maintenance Optimization.
Inventors include Dr. K. Radhika; Mrs. R. Rajgowri; Dr. N. S Aravanan; Dr. K. Saranya; Thangaveni K; Viveka S; Anu A; Praveenkumar H; Sishrutha Raechal G R; Rithesh Kumar J; and Anbumani M.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention relates to a Cognitive Asset Surveillance System for real-time equipment degradation detection and maintenance optimization within industrial environments. The system comprises a distributed sensing infrastructure, real-time information acquisition framework, edge computing architecture, cognitive analytics engine, degradation detection subsystem, asset health intelligence module, predictive intelligence framework, remaining useful life estimation engine, maintenance optimization engine, and visualization platform. Operational information including vibration characteristics, thermal conditions, acoustic emissions, electrical parameters, lubrication quality indicators, environmental influences, and process variables is continuously acquired from industrial assets and processed through cognitive intelligence models. The cognitive analytics engine establishes contextual relationships among operational variables to identify emerging degradation mechanisms and generate asset health indices. The predictive intelligence framework forecasts degradation progression, evaluates failure probabilities, estimates remaining useful life, and assesses operational risks. The maintenance optimization engine generates intelligent maintenance recommendations and optimized maintenance schedules based on equipment condition, criticality, operational priorities, and resource availability. The invention enables continuous asset surveillance, early degradation detection, predictive maintenance planning, reduced equipment downtime, improved reliability, enhanced operational efficiency, lower maintenance costs, and intelligent decision support for Industry 4.0 industrial ecosystems.
Disclaimer: Curated by HT Syndication.
