Video: Autonomous Non-Destructive Remote Robotic Inspection of Offshore Assets
- Vladimir Ivan (The University of Edinburgh) | Arnau Garriga-Casanovas (Imperial College London) | Wolfgang Merkt (The University of Edinburgh) | Frederic B. Cegla (Imperial College London) | Sethu Vijayakumar (The University of Edinburgh)
- Document ID
- Offshore Technology Conference
- Publication Date
- Document Type
- 2020. Copyright is retained by the author. This document is distributed by OTC with the permission of the author. Contact the author for permission to use material from this document.
- 7 Management and Information, 4 Facilities Design, Construction and Operation, 4.9 Facilities Operations, 7.2.1 Risk, Uncertainty and Risk Assessment, 7.2 Risk Management and Decision-Making
- Asset integrity, NDE sensing, material inspection, EMAC, robot
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Offshore assets suffer from material degradation over their lifetimes. Regular inspections are necessary to prevent failures and to reduce the cost of maintenance. These often require downtime of the asset and can involve risk to human workers who have to be sent to the offshore location. In this work, we present a non-destructive (NDE) system in conjunction with a robotic platform, which can perform inspections of the thickness of a component, for example from the outside of a tank or a pressure vessel. The NDE system consists of a digital acquisition system and an electromagnetic acoustic transducer (EMAT). The EMAT generates an acoustic wave, which reflects from the internal features of the component. The wave is received by the same device. The received signal is then processed by the acquisition system to determine the thickness of the component. The NDE system is integrated with a robotic platform that can autonomously or semi-autonomously perform scans of the asset. The robot platform presented in this work uses sensor fusion, machine vision and state of the art motion planning techniques to build a map of the material quality in 3D. This is achieved by exploiting the precise movement of the robot end-effector along the surface of the asset and then integrating the position of the NDE sensor. The collected data is then presented to the remote operator in a user-friendly way, which allows them to evaluate the state of the asset. We validate this system using material samples with known defects. We performed experiments in a controlled environment, and we demonstrate the system in a case study at a testing facility operated by our industrial partners.