基于灰色系统理论的筒子纱卷绕密度测量误差预测  

Prediction of measuring error of bobbin winding density based on grey system theory

作　　者：周其洪[1,2] 韩伟龙 陈鹏 洪巍 岑均豪 ZHOU Qihong;HAN Weiong;CHEN Peng;HONG Wei;CEN Junhao(Engineering Research Center of Advanced Textile Machinery,Ministry of Education,Donghua University,Shanghai201620,China;College of Mechanical Engineering,Donghua University,Shanghai201620,China;Guangzhou Seyounth Automation Technology Co.,Ltd.,Guangzhou,Guangdong511400,China)

机构地区：[1]东华大学纺织装备教育部工程研究中心,上海201620 [2]东华大学机械工程学院,上海201620 [3]广州盛原成自动化科技有限公司,广东广州511400

出　　处：《纺织学报》2023年第10期60-67,共8页Journal of Textile Research

基　　金：国家重点研发计划资助项目(2017YFB1304000)。

摘　　要：为提高采用激光扫描建模的筒子纱卷绕密度测量方法的测量精度,提出基于灰色系统理论的测量误差预测方法。通过灰色关联分析脉冲频率、采样周期和参数K与测量误差的相关性,获得满足灰色建模要求的建模参数。根据实际建模结构为非线性和建模因子序列中元素变化幅度较大的特点,基于传统多变量GM(1,N)幂模型,引入背景值优化和分数阶累加生成得到优化后的GM(1,N)幂模型,然后结合粒子群优化算法通过幂指数自适应寻优建立PSGM(1,N)幂模型,利用实际采集数据进行建模精度验证。结合测量误差预测值对卷绕密度测量值进行校正,得到更精确的卷绕密度值。结果表明,相比于传统多变量GM(1,N)幂模型,PSGM(1,N)幂模型的卷绕密度测量误差预测精度提升了48.6%,激光扫描建模方法的测量精度提高了11.7%。Objective In order to improve the measurement accuracy of the density measurement method for package yarn winding using laser scanning modeling,a prediction method for winding density measurement error based on grey system theory was proposed.By analyzing the changes in measurement errors obtained after adjusting measurement parameters in the actual production process,it can be known that the impact of new measurement parameters on the measurement errors of winding density.Improving the measurement accuracy of non-contact winding density measurement methods is very in line with the current demand for high-quality and efficient production.Method By analyzing the correlation between pulse frequency,sampling period,and parameter K with measurement error through grey correlation analysis,the modeling parameters meeting the requirements of grey modeling were obtained.According to the characteristics that the actual modeling structure was nonlinear and the elements in the modeling factor sequence changed greatly,the optimized GM(1,N)(grey power model)was obtained by introducing background value optimization and fractional order accumulation based on the traditional multivariable GM(1,N)power model,and then by combining particle swarm optimization algorithm with power index adaptive optimization to establish PSGM(1,N)(particle swarm optimization of grey power model),and by using actual collected data to verify the modeling accuracy.Results In order to compare and analyze,a classic multivariable GM(1,N)model,a traditional multivariable GM(1,N)power model and a multivariable PSGM(1,N)power model were established to predict the measurement errors in winding density of three different specifications of bobbin yarns.The modeling accuracy and prediction accuracy of these three models were analyzed and compared.In terms of modeling accuracy or model prediction,the optimized multivariate PSGM(1,N)power model had significantly higher modeling and prediction accuracy than the other two models.Combining the predicted measurement e

关 键 词：筒子纱 卷绕密度 灰色系统理论 粒子群优化算法 背景值优化 分数阶累加生成 误差预测 

分 类 号：TS19[轻工技术与工程—纺织化学与染整工程]