MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541133476 A) filed by Srinivasa Ramanujan Institute Of Technology, Ananthapuramu, Andhra Pradesh, on Dec. 30, 2025, for 'ai driven predictive maintenance system for industrial machinery.'
Inventor(s) include Dr. B. Anjaneyulu; Y. Venkata Siva Krishna; Dr. K. Mahaboob Peera; Mr Pagidipalle Shajahan; Mr. P. Venkatsuneel; and Dr. S. Kannappan.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses an Artificial Intelligence (AI)-driven predictive maintenance system designed for industrial machinery to enhance operational reliability, reduce downtime and optimize lifecycle management. The system integrates real time sensor data acquisition, advanced machine learning algorithms and cloud-based analytics to continuously monitor the condition of industrial assets. The invention employs a multi layered data processing framework wherein vibration, temperature, acoustic signals, current consumption and other machine health parameters are collected through embedded sensors and transmitted to an intelligent processing unit. The AI engine utilizes supervised and unsupervised learning models to identify anomalous patterns, classify potential faults and generate predictive insights regarding component wear, degradation or imminent failure. A distinguishing feature of this system is its adaptive learning capability, wherein the predictive models self update based on feedback from actual maintenance activities and evolving machine usage conditions, thereby improving diagnostic accuracy over time. The system further incorporates a decision-support module that recommends optimal maintenance schedules, spare part requirements, and resource allocation. Integration with Industrial Internet of Things (IIoT) platforms enables remote monitoring, cross-facility benchmarking and centralized control of multiple assets. The invention significantly reduces unplanned machine downtime, minimizes unnecessary preventive maintenance and extends equipment life, resulting in improved productivity and cost savings for industries such as manufacturing, energy, mining and transportation. Additionally, the system enhances workplace safety by proactively identifying hazardous operating conditions before failure occurs. This AI-driven predictive maintenance system offers scalability for various types of industrial equipment and can be implemented as a standalone embedded unit or as a cloud-based service accessible through web and mobile applications. By combining intelligent data analytics with real-time monitoring, the invention provides a robust and adaptive solution for modern industries seeking to transition from reactive or scheduled maintenance approaches to a fully predictive and autonomous maintenance paradigm."
Disclaimer: Curated by HT Syndication.
