MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641061827 A) filed by Vignan's Institute Of Information Technology, Visakhapatnam, Andhra Pradesh, on May 15, for 'ai-based predictive maintenance and service coordination system.'
Inventor(s) include Ramaraju S. V. S. V. Palla; Puppala Sasi Preethi; Boni Hemanth; Mutcherla Manogna Naga Sri; and Narne Uday Kiran.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "Modern vehicles generate large volumes of sensor data, yet most maintenance systems remain reactive, addressing issues only after failures occur. This project presents an AI-driven Predictive Vehicle Maintenance System designed to detect potential faults before breakdowns, provide understandable explanations to drivers, notify them proactively, and offer analytical insights for manufacturers. The system simulates real-time vehicle telemetry using synthetic sensor data such as engine temperature, RPM, speed, coolant level, and battery voltage. This data is ingested through a REST API and stored in a database. A machine learning model is trained to predict whether a vehicle is operating under normal conditions or is at high risk of failure. To avoid black-box predictions, an explainability module converts model outputs into human-readable reasons that can be directly communicated to drivers. Beyond prediction, the system includes a Driver Notification Mechanism demonstrated through a dashboard, a Root Cause Analysis (RCA) and Corrective and Preventive Action (CAPA) module to detect recurring faults across vehicles, and a UEBA-based monitoring module to observe abnormal system behavior for security purposes. All components are visualized using an interactive Stream lit dashboard, enabling real-time demonstration of the complete workflow. Although synthetic data is used for safe and controlled prototyping, the system architecture is designed to seamlessly integrate real vehicle sensor data. This project demonstrates how AI, explainability, system intelligence, and security monitoring can be combined to transform vehicle maintenance from a reactive process into a predictive, explainable, and proactive system that benefits drivers, service centers, and manufacturers alike."
Disclaimer: Curated by HT Syndication.
