A New System for Steam Boiler Tubes using Artificial Neural Network
Gurusamy Pandian1, A. Akhtar Kalam2
1PG.Gurusamy Pandian, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2A.Akhtar Kalam, School of Engineering and Science Victoria University, Melbourne, Australia.
Manuscript received on 24 November 2019 | Revised Manuscript received on 18 December 2019 | Manuscript Published on 30 December 2019 | PP: 873-875 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10601291S419/19©BEIESP | DOI: 10.35940/ijeat.A1060.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A major inspection challenge facing the boiler industries is to satisfy the welds in tubing and plate during the manufacturing, erection and commissioning stages. During commercial operation the plant boiler tubes that leaks due to downtime. As the main part of the boiler, the water wall tube which runs on dangerous environment may lead to serious boiler accidents, causing of the tubes failure. For this reason, inspecting the defects on the steam boiler tube comprehensively is extremely required. However, single traditional NDT inspection method is limited .In terms of the ability of identifying different kinds of defects. In order to meet the requirement of fullscale inspection, in this paper, a new system foe steam boiler tubes are experimented. After experiment, the new system can effectively measure the remaining wall thickness and identify different kinds of defects including pinholes and circumferential cracks with the sensor being moved outside the tubes using artificial neural networks.
Keywords: Water Wall Tubes, Combination, NDT Inspection Method, Artificial Neural Networks.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques