Intellectual Property India Publishes Patent Application for Digital Twin-Driven Autonomous Mechanical Intelligence System For Real-Time Machine Health Prediction, Adaptive Control, And Energy-Efficient Industrial Operations

MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108101 A) filed by Madhankumar C; Dr. Vijayan Gopalsamy - Meenakshi Sundararajan Engineering College; Dr. T. Prabakaran - Meenakshi Sundararajan Engineering College; Dr. S. Kamatchisankaran - Meenakshi Sundararajan Engineering College; Mr. A. Daniel Asirvatham - Meenakshi Sundararajan Engineering College; Dr. Srimanickam Baskaran - Meenakshi Sundararajan Engineering College; and Mr. V. Arul - Meenakshi Sundararajan Engineering College on September 09, 2026, for Digital Twin-Driven Autonomous Mechanical Intelligence System For Real-Time Machine Health Prediction, Adaptive Control, And Energy-Efficient Industrial Operations.

Inventors include Dr. Vijayan Gopalsamy - Meenakshi Sundararajan Engineering College; Dr. T. Prabakaran - Meenakshi Sundararajan Engineering College; Dr. S. Kamatchisankaran - Meenakshi Sundararajan Engineering College; Mr. A. Daniel Asirvatham - Meenakshi Sundararajan Engineering College; Dr. Srimanickam Baskaran - Meenakshi Sundararajan Engineering; and Mr. V. Arul - Meenakshi Sundararajan Engineering College.

The application for the patent was published on September 11, 2026, under issue no. 37/2026.

Abstract: Digital Twin-Driven Autonomous Mechanical Intelligence System for Real-Time Machine Health Prediction, Adaptive Control, and Energy-Efficient Industrial Operations ABSTRACT The present invention relates to a Digital Twin-Driven Autonomous Mechanical Intelligence System for Real-Time Machine Health Prediction, Adaptive Control, and Energy Efficient Industrial Operations, configured to provide continuous monitoring, prediction, optimization, and adaptive control of industrial machines and mechanical systems. The system integrates distributed IoT sensors, machine-condition monitoring, edge computing, digital twin modelling, artificial intelligence, predictive analytics, autonomous control, energy optimization, and explainable decision-support mechanisms within a unified industrial architecture. The system acquires real-time mechanical and operational parameters including vibration, temperature, pressure, torque, rotational speed, load, acoustic signals, current consumption, energy consumption, lubrication condition, and other machine-specific parameters. An edge processing module performs signal conditioning, filtering, synchronization, feature extraction, and preliminary anomaly detection before transmitting processed information to a digital twin platform. A digital twin engine generates a continuously synchronized virtual representation of the physical machine, including its operating state, component condition, historical behaviour, maintenance information, energy profile, and inter component dependencies. An artificial intelligence analytics engine analyses real-time and historical digital twin information to perform machine-health assessment, anomaly detection, remaining useful life prediction, failure prediction, degradation analysis, and operational performance forecasting. An autonomous mechanical intelligence module evaluates predicted machine conditions and dynamically determines suitable operating parameters, maintenance priorities, workload distribution, and energy-saving actions within predefined safety and control constraints. An adaptive control module adjusts machine operating parameters based on real-time condition, production requirements, predicted degradation, and energy consumption patterns. An energy optimization module analyses power and machine-load profiles to reduce unnecessary energy consumption while maintaining defined production and operational requirements. An explainable analytics module provides human-readable information identifying sensor observations, machine-health indicators, contributing factors, prediction confidence, and reasoning paths associated with generated predictions or control recommendations. A monitoring interface provides authorized industrial personnel with real time machine-health indicators, predictive maintenance information, energy-efficiency metrics, alerts, digital twin visualization, and actionable recommendations. The invention thereby enables predictive, adaptive, autonomous, transparent, and energy-efficient operation of industrial mechanical systems while maintaining configurable safety limits and human oversight for critical control actions.

Disclaimer: Curated by HT Syndication.