| 董晓洁1， 贾江鸣1， 贺磊盈1*， 万昌江1,2.基于盲源分离的纱线张力信号去噪研究[J].轻工机械,2024,42(4):69-74 |
| 基于盲源分离的纱线张力信号去噪研究 |
| Study on Denoising Yarn Tension Signal Based on Blind Source Separation |
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| DOI：10.3969/j.issn.1005 2895.2024.04.010 |
| 中文关键词: 纺织机械 纱线张力 经验模态分解 快速独立成分分析 盲源分离 |
| 英文关键词:textile machinery yarn tension EMD（empirical modal decomposition） FastICA（fast independent component analysis） blind source separation |
| 基金项目:浙江省科学技术厅重点研发计划项目选定委托项目(2022C01065)。 |
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| 摘要点击次数: 132 |
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| 中文摘要: |
| 针对采集到的纱线张力信号精确度低、对张力的大小读取困难的问题，课题组提出一种经验模态分解(empirical modal decomposition，EMD)、[JP2]奇异值分解(singular value decomposition，SVD)、快速独立成分分析(fast independent component analysis，FastICA)相结合的纱线张力信号盲源分离方法。[JP]应用经验模态分解方法对张力信号进行自适应分解，得到多个平稳、有线性特点的本征模态函数（intrinsic mode function，IMF）分量；将本征模态函数与张力信号组成多维观测信号，对其协方差矩阵进行奇异值分解，计算邻近奇异值差值并确定源信号的数目；计算IMF分量与张力信号间的相关系数，选择IMF分量与张力信号重构，得到虚拟的多通道信号；对得到新的多通道观测信号进行快速独立成分分析运算，实现纱线张力信号的噪声分离；搭建实验平台去噪实验对该算法进行分析验证。结果表明：该方法实现了纱线张力信号的有效分离，信噪比得到了提高，与15层小波去噪相比，信噪比提高了2.678 1 dB，完成了纱线张力自由振动信号的噪声去除。 |
| 英文摘要: |
| To address the problem of low accuracy of the collected yarn tension signal and the difficulty in reading the tension value, a blind source separation method of the yarn tension signal combined with empirical modal decomposition (EMD), singular value decomposition (SVD) and fast independent component analysis (FastICA) was proposed. The empirical modal decomposition method was applied to adaptive decomposition of tension signal to obtain intrinsic modal function (IMF) components which have smooth and linear characteristics. The intrinsic modal function and the tension signal were formed into a multidimensional observation signal, and covariance matrix was decomposed by singular value decomposition to calculate the adjacent singular value differences and determine the number of source signals. The correlation coefficients between the IMF components and the tension signals were calculated, and the IMF components were selected to be reconstructed with the tension signals to obtain new multichannel signals. Fast independent component analysis was performed on the obtained multichannel observation signals to achieve noise separation of the yarn tension signals. The experimental platform denoising experiment was built to verify the algorithm. The results show that the method can effectively separate the yarn tension signal and improve the signal to noise ratio. The signal to noise ratio is improved by 2.678 1 dB compared with the 15 layer wavelet decomposition denoising method, which completes the noise removal of the yarn tension free vibration signal. |
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