基于经验模态分解的织造过程数据拟合方法的应用

为提高织造过程中的纺织品品质，以保证整个制造过程的连续性，首先，对已有监控系统的应用情况进行了分析；然后，针对系统在数据拟合处理和异常情况预警方面的不足，对织造过程中的不确定性原因进行了深入研究，并对系统已使用的软件滤波算法进行了比较分析。基于此，借助经验模态分解（EMD）方法对织造过程数据进行了去噪实验比较，以及拟合结果仿真。结果表明：基于EMD的数据拟合方法能更准确地保证织造过程数据的正确性，有利于提高纺织品的品质和整个制造过程的连续性，从而加速了纺织企业从“劳动密集型”向“技术密集型”和“品牌密集型”的转型。

Application options of data fitting methods in weaving process based on Empirical Mode Decomposition

To improve the quality of textile weaving process, and to guarantee the continuity of the entire manufacturing process, first, the application of the existing monitoring system was analyzed. Then, for the system deficiencies in data fitting processing and abnormal cases warning, the uncertainty factors in the manufacturing process were studied deeply, and the used software filtering algorithms in the system were compared and analyzed. Furthermore, through using empirical mode decomposition (EMD), de-noising experiments on weaving process data were compared, and the fitting results were simulated. Practice has proved that EMD-based method can effectively ensure the accuracy of production data, improve the quality of the textiles and guarantee the continuity of the entire manufacturing process. Meanwhile, the results can be benefit for accelerating the transition from 'labor-intensive' to 'technology-intensive' and 'brand-intensive' of the textile enterprises.