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Real-time fault detection of braiding ropes using recognition methods

机译:使用识别方法的编织绳实时故障检测

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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.
机译:本文的解决是通过解决一种能够实时检测编织绳故障的装置而引起的。开发了许多用于纺织工业的各种检查装置。然而,生产绳索的纺织公司对智能检测设备的需求不断增长,该设备能够在整理过程中检测出故障。卷绕速度为50 -200 m / min。如今,商业设备专注于纺织面料(机织或针织),它们只能检测基本故障(孔,脏污和油斑)。然而,在几篇研究论文中可能会发现考虑到织物结构故障的情况,但仅针对特定类型的纺织品或仅针对缓慢的过程。在我们的实验室中开发的检查设备能够在绳索的高缠绕速度下工作。该设备基于带有Camera-Link接口的快速线扫描相机。该项目的目标是寻找三个基本的结构缺陷:缺失的股线,股线拉紧和线迹不规则。故障检测的原理是基于收集用于识别方法的最合适的症状。这些方法对于语音识别非常成功,使用它们甚至比使用神经网络为我们带来更好的结果。本文介绍了找到最合适症状的方法,其统计评估和决策算法。最重要的步骤是将问题从耗时的图像处理减少到一维信号处理。

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