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An Improved Independent Component Analysis Algorithm and Its Application in Preprocessing of Bearing Sounds

机译:一种改进的独立分量分析算法及其在轴承声的预处理中的应用

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Independent Component Analysis (ICA) is known as an efficient technique to separate individual signals from various sources without knowing their prior characteristics. Firstly, the basic principle of ICA is reviewed in Sec 2, and then an improved ICA algorithm based on coordinate rotation (CR-ICA) is proposed. Secondly, two advantages of the CR-ICA algorithm are discussed; the one is that the separation can be carried out without iteration, and the other is that less computation is needed to achieve the same effect. Finally, the experiment in recognition of mixed sound and practical application in preprocessing of bearing sounds proved that the CR-ICA algorithm is better than traditional ICA algorithm in separation precision and computation speed. Moreover, the advantages of the method and the potential for further applications are discussed in the conclusion.
机译:独立的分量分析(ICA)被称为一种有效的技术,可以在不知道其现有特征的情况下将各个信号源分离各个来源的有效技术。首先,在Sec 2中审查了ICA的基本原理,然后提出了一种基于坐标旋转(CR-ICA)的改进的ICA算法。其次,讨论了CR-ICA算法的两个优点;一个是在没有迭代的情况下进行分离,另一个是需要较少的计算来实现相同的效果。最后,在轴承声音预处理中识别混合声音和实际应用的实验证明,CR-ICA算法优于传统的ICA算法,分离精度和计算速度。此外,在结论中讨论了方法的优点和进一步应用的潜力。

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