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采用机器视觉与张力观测器相结合的动态纱线张力检测

Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer.

机构信息

School of Control Science and Engineering, Tiangong University, Tianjin 300387, China.

Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, China.

出版信息

Sensors (Basel). 2023 Apr 7;23(8):3800. doi: 10.3390/s23083800.

DOI:10.3390/s23083800
PMID:37112140
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10143009/
Abstract

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton's principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

摘要

机器视觉可以防止接触式测量对纱线造成的额外张力，以及毛羽和断头的风险。然而，机器视觉系统的速度受到图像处理的限制，并且基于轴向运动模型的张力检测方法没有考虑电机振动对纱线的干扰。因此，提出了一种将机器视觉与张力观测器相结合的嵌入式系统。利用哈密顿原理建立弦的横向动力学微分方程，并对其进行求解。采用现场可编程门阵列（FPGA）进行图像数据采集，采用多核数字信号处理器（DSP）实现图像处理算法。为了获得轴向运动模型中纱线的振动频率，提出了纱线图像中最亮的中心线灰度值作为参考，以确定特征线。然后，将计算得到的纱线张力值与基于可编程逻辑控制器（PLC）中自适应加权数据融合方法的张力观测器得到的值相结合。结果表明，与原始的两种非接触式张力检测方法相比，组合张力的精度得到了提高，同时更新速率更快。该系统缓解了仅使用机器视觉方法时采样率不足的问题，可应用于未来的实时控制系统。

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