| Autor: |
Andrew J. Petruska, Lucas Utke, Zhenzhen Yu, Erzhuo Gao, Jiaoyang Li, Lalita Udpa, Ciaron Hamilton, Yiming Deng, Xiaodong Shi, Anthony Olvera, Hao Zhang |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Materials Evaluation. 79:739-751 |
| ISSN: |
0025-5327 |
| Popis: |
The aim of this paper is to develop the concept and a prototype of an intelligent mobile robotic platform that is integrated with nondestructive evaluation (NDE) capabilities for autonomous live inspection and repair. In many industrial environments, such as the application of power plant boiler inspection, human inspectors often have to perform hazardous and challenging tasks. There is a significant chance of injury, considering the confined spaces and limited visibility of the inspection environment and hazards such as pressurization and improper water levels. In order to provide a solution to eliminate these dangers, the concept of a new robotic system was developed and prototyped that is capable of autonomously sweeping the region to be inspected. The robot design contains systematic integration of components from robotics, NDE, and artificial intelligence (AI). A magnetic track system is used to navigate over the vertical steel structures required for examination. While moving across the inspection area, the robot uses an NDE sensor to acquire data for inspection and repair. This paper presents a design of a portable NDE scanning system based on eddy current array probes, which can be customized and installed on various mobile robot platforms. Machine learning methods are applied for semantic segmentation that will simultaneously localize and recognize defects without the need of human intervention. Experiments were conducted that show the NDE and repair capabilities of the system. Improvements in human safety and structural damage prevention, as well as lowering the overall costs of maintenance, are possible through the implementation of this robotic NDE system. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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