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首页> 外文会议>International Conference on Computational Intelligence and Security >Method of Multi-resolution and Effective Singular Value Decomposition in Under-determined Blind Source Separation and Its Application to the Fault Diagnosis of Roller Bearing
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Method of Multi-resolution and Effective Singular Value Decomposition in Under-determined Blind Source Separation and Its Application to the Fault Diagnosis of Roller Bearing

机译：欠定盲源分离中的多分辨率有效奇异值分解方法及其在滚动轴承故障诊断中的应用

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摘要

Considering the background of the roller bearing's compound fault with strong noise, it is difficult to determine the actual fault when the machine has something wrong. In this paper, we propose a method which is called as multi-resolution and effective singularity value decomposition in under-determined blind source separation. Firstly, we put the one-channel compound fault signal into several components through the method of multi-resolution singular value decomposition. By improving the signal's dimension, we solve the limitation of the classical blind source separation method in under-determined case. That is to say the observed signal numbers are less than source numbers. Then we use the effective singularity value decomposition about the track matrix of attractor reconstructed by every component's phase space to detect abrupt information. After that, we apply the method of envelope spectrum analysis to the reconstructed signals to pick out the active ingredients, which include the fault characteristic frequency, to get the new mixed matrix. By this way, we have realized the noise reduction. Finally, we use the method of blind source separation based on kurtosis which is called as Robust Independent Component Analysis to make the new mixed matrix separate. Besides, the application to the roller bearing' compound fault shows that the method we proposed can extract the weak faults from the severe conditions and make accurate judgment for the types of fault.
机译：考虑到带有强噪声的滚动轴承复合故障的背景，当机器出现故障时，很难确定实际故障。在本文中，我们提出了一种在不确定的盲源分离中被称为多分辨率和有效奇异值分解的方法。首先，通过多分辨率奇异值分解的方法，将单通道复合故障信号分解为多个分量。通过改善信号的尺寸，我们解决了在不确定情况下经典盲源分离方法的局限性。也就是说，观察到的信号号小于源号。然后，利用每个分量的相空间重构的吸引子轨迹矩阵的有效奇异值分解，检测突变信息。然后，将包络频谱分析方法应用于重构信号，以提取包括故障特征频率在内的有效成分，得到新的混合矩阵。这样，我们已经实现了降噪。最后，我们使用基于峰度的盲源分离方法（称为鲁棒独立分量分析）将新的混合矩阵分离。此外，在滚动轴承复合故障中的应用表明，本文提出的方法能够从严酷条件下提取出较弱的故障，并对故障的类型做出准确的判断。

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