MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641073560 A) filed by St. Josephs College Of Engineering; Mr. H. Umesh Prabhu; Rishivardani V S; and Shobhana C on June 13, 2026, for Ai-Assisted Smart Industrial Digital Twin For Motor Condition Monitoring And Predictive Fault Detection Using Pic Microcontroller.
Inventors include St. Josephs College Of Engineering; Mr. H. Umesh Prabhu; Rishivardani V S; and Shobhana C.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The rapid growth of Industry 4.0 technologies has increased the demand for intelligent monitoring and predictive maintenance systems in industrial environments. Industrial motors are widely used in manufacturing plants and are prone to failures caused by overheating, excessive current, vibration, and mechanical wear. Conventional monitoring methods are unable to provide early fault prediction, resulting in unexpected downtime and increased maintenance costs. This project presents an AI-Assisted Smart Industrial Digital Twin system for motor condition monitoring and predictive fault detection using a PIC microcontroller. The system continuously acquires real-time motor parameters such as temperature using LM35, current using ACS712, and vibration using a vibration sensor. The collected data is processed by the PIC microcontroller and transmitted wirelessly through the ESP8266 WiFi module to a laptop-based digital twin dashboard. The digital twin creates a virtual representation of the motor and displays real-time operating conditions using graphs and live visualization. AI-based anomaly detection techniques are implemented to identify abnormal operating patterns and predict possible faults before actual failure occurs. The system also provides safety alerts through LEDs, buzzers, and warning notifications during abnormal conditions. The proposed system offers a low-cost, intelligent, and efficient solution suitable for small-scale industries by combining IoT, AI, and digital twin technology for predictive maintenance and improved motor reliability.
Disclaimer: Curated by HT Syndication.
