Abstract:

For a more accurate yarn diameter prediction and accurate yarn quality prediction, the principle of yarn diameter data sampling was firstly analyzed, piecewise polymerization of yarn sample fragments was performed, and a time series model state equation was established based on the yarn diameter value after polymerization. Next, the yarn diameter and the coefficient of variation were predicted using autoregressive moving average model. Then the predicted value was optimized using Kalman filter. The accuracy of the prediction model was verified through experiments, and the results showed that the root mean square error of the yarn diameter predicted after Kalman filter optimization was 2.68%, with an average absolute percentage error of 6.71%. Compared with the yarn unevenness predicted by other methods, this method exhibited excellent prediction accuracy. The test results of the eight experimental samples selected for model generalization verification were all within 50% of Uster statistical value, with the error between the average yarn diameter and the theoretical diameter of less than 3%, indicating that the prediction model is accurate when applied to yarn quality online prediction. This prediction model can be used as a new method for yarn quality prediction.

Key words: piecewise polymerization, time series, Kalman filter, yarn diameter, data prediction

摘要：

为准确预测纱线直径,提高纱线质量预测的准确度,首先对纱线直径数据采样原理进行分析,对纱线样本片段分段聚合,利用聚合后的纱线直径值建立时间序列模型状态方程,采用自回归滑动平均模型ARMA(p,q)进行纱线直径和变异系数预测,然后利用卡尔曼滤波对预测值进行优化。通过实验对预测模型进行准确性验证,结果表明:卡尔曼滤波优化后预测的纱线直径均方根误差为2.68%,平均绝对百分比误差为6.71%;比对其他预测方法预测的条干不匀率,显示出良好的预测精度;模型泛化验证所选取的8个实验样本的检测结果均在乌斯特50%统计值内,同时纱线平均直径与理论直径之间的误差小于3%。这表明该预测模型对于在线预测纱线质量具有一定的准确性,为预测纱线质量提供一种新方法。

关键词: 分段聚合, 时间序列, 卡尔曼滤波, 纱线直径, 数据预测