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Proceedings of CAD'17, 2017, 242-246
Detection of Deterioration of Furnace Walls Using Large-Scale Point-Clounds

Yuki Shinozaki, Keisuke Kohira, Hiroshi Masuda, The University of Electro-Communications

Abstract. Blast furnaces are large industrial structures to produce iron. Since scaffolding and wearing of furnace walls are caused in their long life cycle, furnaces have to be repeatedly renovated. In conventional diagnosis of furnaces, inspectors visually estimate the amount of scaffolding and wearing, and identify a need for maintenance. However, conventional diagnosis is strongly dependent on the skill of each inspector. The state-of-the-art terrestrial laser scanners can capture dense point-clouds from large-scale facilities in a short time. They are promising to precisely estimate the amount of scaffolding and wearing of furnace walls. To obtain the amount of scaffolding and wearing, we have to estimate reference surfaces, which are the original wall shapes with no scaffolding and wearing. However, in most cases, furnaces were not precisely built as designed in drawings even though most furnace walls were originally designed as a combination of nominal rotational surfaces, such as cylinders and cones. Therefore, when we suppose nominal surfaces for furnace walls, construction errors may be incorrectly detected as scaffolding or wearing parts.

Keywords. Point-Cloud, Maintenance, Point Processing, Deterioration Diagnosis, Terrestrial Laser Scanner

DOI: 10.14733/cadconfP.2017.242-246