摘要： 为确保织造过程坯布的质量稳定性和产量数据采集的准确性,对已有织造数据拟合方法进行应用对比分 析,针对其在非线性织机声信号处理方面的不足,从不确定因素形成机理的角度对影响坯布质量波动的原因进行 理论分析。利用经验模态分解算法在非线性信号处理方面的优势,构建一种改进的在线织造数据拟合方法,并将 其应用于织机声信号特征的实时提取。实验结果表明,与现有数据拟合方法相比,该方法拟合处理后的坯布质量 明显提高,有效确保织造过程坯布质量的稳定性和产量数据采集的准确性。

关键词: 数据拟合, 织造过程, 生产监控, 经验模态分解, 信号处理

Abstract: To ensure the stability of fabric quality and the accuracy of yield data acquisition in the weaving process,the existed data fitting methods are comparatively analyzed,and aiming at the shortage in the nonlinear loom sound signal processing aspects of these methods,the reasons why the fabric quality fluctuates are theoretically analyzed from the formation mechanism of uncertainty factors. Being combined with the advantage in the nonlinear signal process of Empirical Mode Decomposition(EMD) algorithm,an online weaving data fitting method based on EMD is constructed,and EMD is applied in the real-time acoustic signal characteristic extraction process of weaving machinery. Experimental results show that compared with the existed data fitting methods,the method of EMD significantly improves the fabric quality index,and effectively ensures the stability of fabric quality in the weaving process,and the accuracy of yield data acquisition.

Key words: data fitting, weaving process, production monitoring, Empirical Mode Decomposition(EMD), signal processing

中图分类号: 

• TP18