一种高鲁棒性经编机上断纱在线检测算法

Robustness algorithm for online yarn breakage detection in warp knitting machines

为检测经编机编织过程中产生的断纱问题,提出了以图像维度转换为基础的复合检测算法。首先通过不同时刻拍摄的多张图像生成一张包含时域信息的特征增强图像,以减少环境噪声带来的影响,并极大程度保持了断纱位置时域与频域的特征;然后使用小波变换的方式对增强图像进行断纱检测,在保证检出的情况下提升了检测速度;最后对检测到缺陷的位置使用深度学习的方式再进行一次检测以提升整体算法的鲁棒性。实验结果表明,本文算法可有效地检测不同工艺多类型纹理织物的断纱特征,相对于传统算法时效性与鲁棒性都得到很大程度的提高,能够满足工业场景下断纱检测的需要。

Objective For warp knitting machines,yarn breakage is inevitable in the working process.When a yarn breakage occurs,the warp knitting machine should be stopped immediately for yarn repair so as to avoid causing fabric defects.However,because the yarn diameter is only tens of microns,and tens of thousands of yarns are knitted at high speed at the same time in the knitting process,it brings great difficulties to the online detection of yarns in warp knitting machines.Method Aiming at the above problem,this paper proposes a composite detection algorithm.Firstly,the defect feature enhancement method based on dimension transformation was adopted to enhance the stability of the algorithm by data processing.Secondly,on the basis of dimensional transformation data,a yarn breakage detection algorithm based on wavelet transformation was proposed,and the gray level transformation rule of the image was analyzed from the perspective of time domain to realize defect detection.Finally,deep learning was adopted to further improve the robustness of the algorithm.Results To verify the effectiveness of the proposed algorithm,the detection system was built in KARL MAYER RD7/2-12 warp knitting machine(Fig.8).Ten cameras were adopted to shoot at a distance of 1 m.Five groups of experiments were carried out to verify the feasibility of the algorithm under different processing conditings(Tab.1).The first group of data was taken as an example and the detection processes(Fig.10).Five sets of yarn breakage experiments were conducted on three different warp knitting machines,and the methods proposed in this paper can effectively identify yarn breakage.It is shown that the algorithm proposed in this paper can effectively detect yarn breakage.The algorithm proposed in this paper was comared with STL(time series decomposition algorithm)and wavelet decomposition algorithm(Tab.2).The timeliness of different algorithms was analysed,indicating that the conventional STL decomposition algorithm had the worst timeliness(Tab.2).The proposed method and wavelet decomposition showed better timeliness,and it also proved that the introduction of deep learning had no impact on the timeliness of the algorithm.About 120 h of continuous experiment was set to verify the stability of the algorithm proposed in this paper,compared with other algorithms,the missed detection rate and the false detection rate were both decreased(Fig.11).Conclusion Aiming at the problem of online detection of broken yarn defects in warp knitting machines,this paper proposes a robustness detection algorithm,which is proven to be feasible by a large number of experiments.An innovative method of weak defect feature enhancement based on dimension transformation is proposed to overcome the degradation of detection stability caused by various weaving processes and serious external noise interference.The problem of restricting the accuracy and timeliness of broken yarn detection on warp knitting machines has been solved.The proposed algorithm has important implications for textile defect detection.At the same time,the proposed algorithm is expected to promote the further development of textile industry towards automation and intelligence.