MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202621064755 A) filed by Mr. Chander Vijay S Sanbhi on May 22, 2026, for Edge Computing Platform For Predictive Maintenance Of Remote And Offshore Assets With Autonomous 5g-Enabled Sensor Networks.
Inventor includes Mr. Chander Vijay S Sanbhi.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: ABSTRACT [505] Modern industrial maintenance paradigms for remote and offshore assets—including offshore drilling platforms, wind farms, subsea pipelines, and isolated pumping stations—face a foundational operational crisis rooted in unreliable connectivity, high-latency cloud dependency, and the exponential growth of condition-monitoring sensor data. Traditional cloud-centric predictive maintenance architectures cannot guarantee real-time fault detection when bandwidth is limited or satellite links are intermittent, resulting in catastrophic unplanned downtime, safety hazards, and multi million dollar production losses. [510] Existing edge computing solutions exhibit critical deficiencies in their capacity to autonomously orchestrate 5G-enabled sensor networks under dynamic environmental conditions, execute distributed real time anomaly detection across heterogeneous sensor modalities (vibration, thermal, acoustic, ultrasonic, and corrosion), adapt predictive models to evolving asset degradation patterns without cloud retraining, and reconcile conflicting maintenance recommendations from geographically distributed edge nodes. Consequently, offshore asset operators remain trapped in reactive maintenance cycles. [515] The convergence of autonomous 5G network slicing, lightweight deep learning inference engines, federated learning at the edge, and digital twin synchronization presents transformative opportunities for revolutionizing predictive maintenance in connectivity-constrained environments. Systems capable of autonomously managing sensor mesh networks, compressing and transmitting only actionable intelligence, and continuously improving local degradation models can eliminate dependency on constant cloud connectivity while achieving real-time fault prediction. [520] The present invention describes a comprehensive Edge Computing Platform for Predictive Maintenance of Remote and Offshore Assets with Autonomous 5G Enabled Sensor Networks. The platform integrates: an autonomous 5G mesh network orchestrator for dynamic sensor node discovery and resource allocation; a lightweight multi modal anomaly detection engine running on ARM based edge accelerators; a federated incremental learning module for local model adaptation without cloud round trips; and a digital twin synchronization agent that prioritizes differential state updates over low bandwidth satellite links. [525] Validation studies conducted across three operational North Sea offshore platforms and two remote onshore pipeline installations demonstrated that the platform achieved 99.3 percent anomaly detection sensitivity, 91.7 percent reduction in satellite bandwidth consumption via intelligent edge filtering, 76.4 percent decrease in mean time to detect critical faults, and 52.8 percent extension of asset component operational life through pre emptive maintenance triggered by early degradation signatures invisible to traditional threshold based alarms. [530] The research findings confirm that the claimed edge computing platform constitutes a foundational technological advancement for predictive maintenance in remote and offshore environments, with deployment potential spanning oil and gas production assets, floating offshore wind turbines, subsea telecommunications infrastructure, remote mining conveyors, and maritime navigation aids requiring autonomous, low latency, bandwidth aware condition monitoring and fault prediction.
Disclaimer: Curated by HT Syndication.
