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Proceedings Paper

Real-time fault detection of braiding ropes using recognition methods
Author(s): Lukas Matela
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Paper Abstract

Formation of this paper is evoked by solving of device that is able to detect faults of braiding ropes in real-time. Many various inspection devices for textile industry were developed. However, rope-producing textile company has come with demand of intelligent inspection device that is able to detect faults in finishing process. The winding speeds are 50 - 200 m/min. Nowadays commercial devices are focused on textile fabrics (weaving or knitting) and they are only able to detect basic faults (holes, dirty and oil spots). Considering textile structure faults are possible to find in several research papers, however, for specific types of textiles or for slow processes only. The inspection device, which has been developed in our laboratory, is able to work with high winding speeds of rope. The device is based on fast line-scan camera with Camera-Link interface. The goal of the project was to search three basic structure faults: missing strand, strands pulled tight and stitch irregularity. The principle of fault detection is based on gathering the most suitable symptoms that are used for recognition methods. These methods are very successful for speech recognition and using them even bring us better results than using neural networks. This paper shows the way of finding the most suitable symptoms, their statistical evaluation and decision making algorithms. The most important step is reducing the problem from time-consuming image processing to one-dimensional signal processing.

Paper Details

Date Published: 25 October 2004
PDF: 9 pages
Proc. SPIE 5603, Machine Vision and its Optomechatronic Applications, (25 October 2004); doi: 10.1117/12.570407
Show Author Affiliations
Lukas Matela, Technical Univ. of Liberec (Czech Republic)

Published in SPIE Proceedings Vol. 5603:
Machine Vision and its Optomechatronic Applications
Shun'ichi Kaneko; Hyungsuck Cho; George K. Knopf; Rainer Tutsch, Editor(s)

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