MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641127173 A) filed by Dr Rvr Nri Institute Of Technology Deemed To Be University; Dr. J. Rajendra Prasad; D. Yasaswini; Ch. Mahesh Babu; B. Keerthi; Ch. Phaneendra; and A. Pavan Kumar on October 26, 2026, for Aquanex: An Ai-Assisted Pipeline Damage Detection And Maintenance Decision-Support System.
Inventors include Dr. J. Rajendra Prasad; D. Yasaswini; Ch. Mahesh Babu; B. Keerthi; Ch. Phaneendra; and A. Pavan Kumar.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: ABSTRACT [0030] A web-based pipeline inspection and maintenance decision-support system is disclosed for organizing pipeline inspection information and supporting evidence-based maintenance review. The system receives pipeline information, damage photographs, geographic coordinates and inspection remarks, validates required inspection information, and associates the submitted evidence with a structured inspection record. A prototype assessment component provides assessment fields including damage type, severity, confidence and repair-priority information for review. A geographic visualization component presents recorded inspection locations through a map interface. A report management component organizes inspection records and supporting evidence, while a maintenance review workflow enables authorized Maintenance Engineers to examine submitted reports and update maintenance status. The system provides a unified workflow for connecting field inspection observations, image evidence, geographic information and engineering review. This project presents AquaNex, a web-based pipeline inspection and maintenance decision-support platform designed to organize inspection evidence and support maintenance-related review. The application uses a React and Vite interface, Tailwind CSS styling, and Supabase with PostgreSQL for authentication and data management, together with image storage and geographic visualization components. The reported functional evaluation covers authentication and role-specific access, inspection submission with image and location information, presentation of prototype assessment fields, and engineer review and management of submitted reports. The project report describes successful outcomes for these functional checks; however, the available evaluation does not establish dataset-level damage-detection accuracy, model precision or recall, inference latency, or field reliability. The assessment component is therefore described as a prototype, and predictive maintenance, trained damage-detection models, IoT sensor connectivity and enhanced geographic capabilities remain potential future extensions rather than confirmed completed functions.
Disclaimer: Curated by HT Syndication.
