MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641113588 A) filed by J. J College Of Engineering And Technology on September 22, 2026, for Ai-Driven Multi-Sensor Industrial Equipment Fault Detection And Predictive Maintenance System.
Inventors include Mrs. P. Sumathi; Ms. Radhika; Ms. Sneha; Ms. R. Neelaveni; Mr. V. Praveen; Mr. S. Santhosh Kumar; Dr. T. Gurumekala; and Mrs. T. Josephine Arockia Mary.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The present invention relates to an artificial intelligence (AI)-driven multi-sensor system for detecting faults and predicting maintenance requirements in industrial equipment. The system comprises a plurality of sensors configured to acquire heterogeneous operational parameters including vibration, temperature, acoustic emission, motor current, pressure, rotational speed and other equipment-specific parameters. A sensor interface and data acquisition unit receives and synchronizes the sensor signals, while a preprocessing unit performs noise reduction, filtering, normalization, feature extraction and sensor-data validation. An AI-based analysis unit processes the synchronized multi-sensor data using one or more machine-learning models to identify abnormal operating conditions, classify fault types, estimate fault severity and predict remaining useful life of the equipment. A sensor-fusion module combines information from multiple sensing modalities to improve fault detection reliability and reduce false alarms. A predictive maintenance module generates maintenance recommendations based on detected fault conditions, predicted degradation and operating history. A monitoring and communication module provides real-time alerts, equipment health status and maintenance information to an operator or maintenance management system. The system thereby enables early fault detection, condition-based maintenance and predictive maintenance of industrial equipment while reducing unexpected equipment failures, downtime and maintenance costs.
Disclaimer: Curated by HT Syndication.
