MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125660 A) filed by Madhankumar C; Ms Madhumitha D - Rathinam Technical Campus; Ms Kanimozhi S - Rathinam Technical Campus; Ms Charulatha P - Rathinam Technical Campus; and Ms Priyadharshini S - Rathinam Technical Campus on October 22, 2026, for Ai-Based Predictive Maintenance System For Real-Time Industrial Motor Health Monitoring And Failure Prediction.
Inventors include Ms Madhumitha D - Rathinam Technical Campus; Ms Kanimozhi S - Rathinam Technical Campus; Ms Charulatha P - Rathinam Technical Campus; and Ms Priyadharshini S - Rathinam Technical Campus.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: ABSTRACT AI-Based Predictive Maintenance System for Real-Time Industrial Motor Health Monitoring and Failure Prediction The present invention relates to an intelligent predictive maintenance system for real time monitoring of industrial equipment and prediction of potential equipment failures. The proposed system integrates Internet of Things (IoT) based sensor monitoring, data acquisition, data preprocessing, feature extraction, artificial intelligence, machine learning, equipment health assessment, failure prediction, and maintenance decision support to provide proactive maintenance of industrial equipment. The system acquires real-time operating information from a plurality of sensors associated with an equipment unit, wherein the acquired information may include temperature, vibration, current, and other equipment-health parameters. A data processing module performs data filtering, noise removal, synchronization, normalization, and feature extraction on the acquired sensor information before providing the processed information to a predictive intelligence engine. A machine-learning based predictive intelligence engine analyzes historical and real time equipment data to identify normal and abnormal operating patterns and determine the health condition of the monitored equipment. The predictive engine is configured to detect anomalies, classify equipment operating conditions, determine potential fault conditions, and predict the likelihood of equipment failure based on the observed operating parameters. The system further comprises an equipment health assessment and maintenance decision-support module configured to evaluate the predicted condition of the equipment and generate maintenance recommendations. The recommendations may be prioritized according to equipment health condition, predicted failure risk, equipment criticality, operating parameters, historical information, and prediction confidence. The predicted equipment condition, health status, failure risk, and maintenance recommendation may be presented through a visualization dashboard or alert mechanism for enabling timely maintenance action. Accordingly, the proposed invention provides a predictive maintenance framework capable of continuously monitoring equipment operating conditions, identifying abnormal behavior, predicting potential failures, and supporting condition-based maintenance decisions, thereby reducing unexpected equipment downtime, improving equipment reliability, optimizing maintenance activities, and enhancing overall operational efficiency.
Disclaimer: Curated by HT Syndication.
