Article Article
AI-Enabled Robotic NDE for Structural Damage Assessment and Repair

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.



Albawi, S., T.A. Mohammed, and S. Al-Zawi, 2017, “Understanding of a Convolutional Neural Network,” 2017 International Conference on Engineering and Technology (ICET), pp. 1–6, Technol.2017.8308186

Ali, M.S., and H. Habibullah, 2019, “A Review on the Current Status of Boiler Inspection and Safety Issues in Bangladesh,” Energy Procedia, Vol. 160, pp. 614–620,

Ali, R., and Y.-J. Cha, 2019, “Subsurface Damage Detection of a Steel Bridge Using Deep Learning and Uncooled Micro-Bolometer,” Construction and Building Materials, Vol. 226, pp. 376–387,

Baba, T., S. Harada, H. Asano, K. Sugimoto, N. Takenaka, and K. Mochiki, 2009, “Nondestructive Inspection for Boiling Flow in Plate Heat Exchanger by Neutron Radiography,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 605, Nos. 1–2, pp. 142–145,

Bogue, R., 2010, “The Role of Robotics in Non‐destructive Testing,” Industrial Robot, Vol. 37, No. 5, pp. 421–426, /01439911011063236

Byers, L., J. Friedrich, R. Hennig, A. Kressig, X. Li, C. McCormick, and L. Malaguzzi Valeri, 2018, “A Global Database of Power Plants,” World Resources Institute, available at /global-database-power-plants

Caccamo, S., R. Parasuraman, L. Freda, M. Gianna, and P. Ögren, 2017, “RCAMP: A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity,” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2010–2017,

Cui, L., H. Fujii, N. Tsuji, K. Nakata, K. Nogi, R. Ikeda, and M. Matsushita, 2007, “Transformation in Stir Zone of Friction Stir Welded Carbon Steels with Different Carbon Contents,” ISIJ International, Vol. 47, No. 2, pp. 299–306,

De, A., H.K.D.H. Bhadeshia, and T. DebRoy, 2014, “Friction Stir Welding of Mild Steel: Tool Durability and Steel Microstructure,” Materials Science and Technology, Vol. 30, No. 9, pp. 1050–1056, /10.1179/1743284714Y.0000000534

Ghosh, M., K. Kumar, and R.S. Mishra, 2011, “Friction Stir Lap Welded Advanced High Strength Steels: Microstructure and Mechanical Properties,” Materials Science and Engineering: A, Vol. 528, No. 28, pp. 8111–8119,

Gibb, S., H.M. La, T. Le, L. Nguyen, R. Schmid, and H. Pham, 2018, “Nondestructive Evaluation Sensor Fusion with Autonomous Robotic System for Civil Infrastructure Inspection,” Journal of Field Robotics, Vol. 35, No. 6, pp. 988–1004,

He, K., G. Gkioxari, P. Dollár, and R. Girshick, 2017, “Mask R-CNN,” 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988,

Hellier, C., 2013, Handbook of Nondestructive Evaluation, 2nd ed., McGraw Hill

Kharkovsky, S., and R. Zoughi, 2007, “Microwave and Millimeter Wave Nondestructive Testing and Evaluation – Overview and Recent Advances,” IEEE Instrumentation & Measurement Magazine, Vol. 10, No. 2, pp. 26–38,

Korhonen, I., and J. Ahola, 2018, “Microwave Attenuation in Kraft Recovery Boiler,” IET Microwaves, Antennas & Propagation, Vol. 12, No. 2, pp. 241–245,

Kumar, D., S. Karuppuswami, Y. Deng, and P. Chahal, 2018, “A Wireless Shortwave Near-Field Probe for Monitoring Structural Integrity of Dielectric Composites and Polymers,” NDT & E International, Vol. 96, pp. 9–17,

Lian, G., Y. Niu, X. Zhang, Y. Lu, and H. Li, 2018, “Analysis of Causes of Boiler Accidents in Power Plant and Accident Handling Based on Mathematical Statistics,” 2018 International Conference on Engineering Simulation and Intelligent Control (ESAIC), pp. 17–20,

Lienert, T.J., W.L. Stellwag, Jr., B.B. Grimmett, and R.W. Warke, 2003, “Friction Stir Welding Studies on Mild Steel,” Supplement to Welding Journal, pp. 1-S–9-S

Liu, Y., A. Xie, D. Shen, and X. Yang, 2020, “Eddy Current Testing in Service of the Boiler Heating Surface Tube Elbow in Thermal Power Plants,” IOP Conference Series: Earth and Environmental Science, Vol. 585,

Ma, Z.Y., 2008, “Friction Stir Processing Technology: A Review,” Metallurgical and Materials Transactions A, Vol. 39, pp. 642–658,

Mandeliya, M., and M. Vishwakarma, 2018, “A Review on Boiler Tube Assessment in Power Plant Using Ultrasonic Testing,” International Research Journal of Engineering and Technology (IRJET), Vol. 5, No. 6, pp. 708–714

Mattar, R.A., and R. Kalai, 2018, “Development of a Wall-Sticking Drone for Non-destructive Ultrasonic and Corrosion Testing,” Drones, Vol. 2, No. 1,

Meng, H., K. Ouyang, G. Bao, and S. Cai, 2018, “The Inspection Module of Robot for Detecting Large CFB Boiler Furnace Wear,” 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 145–149,

Meyendorf, N.G., L.J. Bond, J. Curtis-Beard, S. Heilmann, S. Pal, R. Schallert, H. Scholz, and C. Wunderlich, 2017, “NDE 4.0—NDE for the 21st Century—the Internet of Things and Cyber Physical Systems Will Revolutionize NDE,” Proceedings of the 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT 2017), Singapore

Miro, J.V., D. Hunt, N. Ulapane, and M. Behrens, 2018, “Towards Automatic Robotic NDT Dense Mapping for Pipeline Integrity Inspection,” Field and Service Robotics, pp. 319–333, -319-67361-5_21

Musyafa, A., and H. Adiyagsa, 2012, “Hazard and Operability Study in Boiler System of the Steam Power Plant,” IEESE International Journal of Science and Technology (IJSTE), Vol. 1, No. 3, pp. 1–10

Natesan, K., 2002, “High-Temperature Corrosion in Power-Generating Systems,” 7th Polish Corrosion Conference Korozja 2002, 17–21 June, Kraków, Poland

Noble, W.S., 2006, “What Is a Support Vector Machine?” Nature Biotechnology, Vol. 24, pp. 1565–1567,

ROS, 2021, “About ROS (Robot Operating System),”, accessed 1 June 2021

Schmidt, M., G. Fung, and R. Rosales, 2007, “Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches,” Machine Learning: ECML 2007, Lecture Notes in Computer Science, Vol. 4701, pp. 286–297,

Ullmann, I., P. Egerer, J. Schür, and M. Vossiek, 2020, “Automated Defect Detection for Non-Destructive Evaluation by Radar Imaging and Machine Learning,” 2020 German Microwave Conference (GeMiC), pp. 25–28

Wu, Z., C. Shen, and A. van den Hengel, 2019, “Wider or Deeper: Revisiting the ResNet Model for Visual Recognition,” Pattern Recognition, Vol. 90, pp. 119–133,

Yu, Z., Y. Fu, L. Jiang, and F. Yang, 2021, “Detection of Circumferential Cracks in Heat Exchanger Tubes Using Pulsed Eddy Current Testing,” NDT & E International, Vol. 121, .2021.102444

Zhang, H., K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal, 2018, “Context Encoding for Semantic Segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7151–7160

Zhang, Y., Y. Gong, Y. Wang, and K. Han, 2020, “Analysis of Nondestructive Testing Method for Transverse Crack of Water Wall of Supercritical Boiler,” IOP Conference Series: Earth and Environmental Science, Vol. 512,

Zhu, P., Y. Cheng, P. Banerjee, A. Tamburrino, and Y. Deng, 2019, “A Novel Machine Learning Model for Eddy Current Testing with Uncertainty,” NDT & E International, Vol. 101, pp. 104–112,

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