Intellectual Property India Publishes Patent Application for Predictive Maintenance System For Medical Devices Using Technical Condition Sensing And Machine Learning Based Failure Risk Control

MUMBAI, India, June 22 -- Intellectual Property India has published a patent application (202641069300 A) filed by Dr. K. B. Venkata Brahma Rao; Dr. S. P. Velmurugan; Ms. Nikshepa T.; Dayananda Sagar Academy Of Technology And Management; Mr. Ranjan P. J.; Dr. R. Gurunadham; Dr. Vijaya Kumar A. V.; Dr. Prabhakar M.; Mr. Bharath V. G.; and Dr. Mahesh Kumar A. S. on June 02, 2026, for Predictive Maintenance System For Medical Devices Using Technical Condition Sensing And Machine Learning Based Failure Risk Control.

Inventors include Dr. K. B. Venkata Brahma Rao; Dr. S. P. Velmurugan; Ms. Nikshepa T.; Mr. Ranjan P. J.; Dr. R. Gurunadham; Dr. Vijaya Kumar A. V.; Dr. Prabhakar M.; Mr. Bharath V. G.; and Dr. Mahesh Kumar A. S..

The application for the patent was published on June 12, 2026, under issue no. 24/2026.

Abstract: The present invention discloses a predictive maintenance system for medical devices using technical condition sensing and machine learning based failure risk control. The system includes a device interface module, sensor data acquisition module, data pre-processing module, machine learning prediction engine, failure risk scoring module, remaining useful life estimation module, maintenance scheduling module, notification module, and audit log module. The system acquires technical operating data such as temperature, pressure, flow, vibration, motor current, voltage, battery behaviour, calibration deviation, fault codes, alarm frequency, runtime duration, and environmental conditions from one or more medical devices. The machine learning prediction engine analyses the processed condition data to detect degradation patterns, estimate remaining useful life, and generate device-specific failure risk scores. The system provides prioritized maintenance actions before actual device failure, thereby reducing unexpected downtime, improving reliability of critical medical equipment, minimizing unnecessary preventive servicing, and supporting secure maintenance documentation without requiring patient-identifiable data. Accompanied Drawing [FIGS. 1-2]

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