Characterization of subsurface cracks in eddy current testing using machine learning methods

2021 ◽  
Author(s):  
Fatima Barrarat ◽  
Karim Rayane ◽  
Bachir Helifa ◽  
Ibn Khaldoun Lefkaier
Keyword(s):  
Machine Learning ◽  
Eddy Current ◽  
Eddy Current Testing ◽  
Learning Methods ◽  
Current Testing ◽  
Subsurface Cracks ◽  
Machine Learning Methods

Development of a four-wheeled robotic platform for autonomous inspection

2020 ◽  
Author(s):  
◽  
Wenxin Gao
Keyword(s):  
Machine Learning ◽  
Heat Exchanger ◽  
Machine Vision ◽  
Eddy Current ◽  
Control Algorithm ◽  
Eddy Current Testing ◽  
Machine Learning Algorithms ◽  
Robotic Platform ◽  
Current Testing ◽  
Autonomous Inspection

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a significant need of developing advanced robots to mitigate the time-consuming and labor-intensive maintenance and examination of the heat exchanger tubes in power plants. Heat exchanger tubes are very critical to energy conversion efficiency of a power plant. Eddy current testing is a mainstream methodology to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert the probes into and extract out of individual tubes, and monitor measurement results in the meantime. This process can be very tedious and requires a large amount of operation time because there are usually a bundle of tubes arranged in a closely packed way. In addition, the eddy current testing equipment requires sophisticated technicians to operate. If the tube inspection can be performed by a robot, significant economic benefit can be obtained. To realize such goal, the proposed robot show possess functions of machine vision enabled by as high-resolution camera image recognition, adaptive motion and actuation enabled by a control-looped control algorithm, autonomous eddy current probe signal analysis, and real-time decision making by machine learning algorithms. This thesis focuses on development of an omni-direction four-wheeled robotic platform for autonomous inspection of exchange heat tubes. Chapter 2 introduces the robotic platform design and analysis. It includes the mechanical components design for the robot, the detailed drawings, finite element analysis (FEA) for critical components, the motion analysis for part movement detection and the overall balance analysis of the robotic platform. Chapter 3 describes the manufacturing and assembly of the robot. Discussion on the part cutting and drilling accuracy, the strategy of choosing the materials, and problem shooting during the assembly such as solving the mismatch between two metal parts due to machining is provided. Chapter 4 shows the control methodology for the wheels and actuation of the wheels according the requirement of omni-direction movement, rotation, and the probe station operation. A circuit schematic diagram and the detailed control algorithm are also included in the content. This chapter also discusses integration of the robot platform and the machine vision. As concluded in the future work, functions of machine vision by the high resolution camera to capture image data, operating the eddy current testing to capture signal data, training machine learning algorithms for autonomous detection of defects inside the tubes are discussed.

A novel machine learning model for eddy current testing with uncertainty

2019 ◽  
Vol 101 ◽  
pp. 104-112 ◽  
Author(s):  
Peipei Zhu ◽  
Yuhua Cheng ◽  
Portia Banerjee ◽  
Antonello Tamburrino ◽  
Yiming Deng
Keyword(s):  
Machine Learning ◽  
Eddy Current ◽  
Learning Model ◽  
Eddy Current Testing ◽  
Current Testing ◽  
Machine Learning Model

Characterization of carbon fiber orientation in surface/subsurface and deep part of CFRP by using eddy current testing

2017 ◽  
Vol 2017.52 (0) ◽  
pp. 166
Author(s):  
Yuki YOSHIKAWA ◽  
Hiroyuki KOSUKEGAWA ◽  
Ryoichi URAYAMA ◽  
Tetsuya UCHIMOTO ◽  
Toshiyuki TAKAGI
Keyword(s):  
Carbon Fiber ◽  
Eddy Current ◽  
Fiber Orientation ◽  
Eddy Current Testing ◽  
Deep Part ◽  
Current Testing
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