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A novel Fast Entrogram and its applications in rolling bearing fault diagnosis
Mechanical Systems and Signal Processing ( IF 7.9 ) Pub Date : 2021-01-07 , DOI: 10.1016/j.ymssp.2020.107582
Kun Zhang , Yonggang Xu , Zhiqiang Liao , Liuyang Song , Peng Chen

Effectively identifying the health status of rolling bearings can reduce the maintenance costs of rotating mechanical components. With the development and improvement of various signal processing theories, the mode of extracting fault information from the frequency domain has gradually replaced the mode from the time domain. As a traditional spectrum segmentation analysis method, Fast Kurtogram can adaptively extract frequency bands that may contain fault information to diagnose faults. However, the frame of the center frequency and bandwidth obtained by the 1/3 binary tree filter bank segmentation method adopted by the Fast Kurtogram is fixed. This paper proposed a new method of segmenting the spectrum and accurately filtering fault information from the frequency domain----Fast Entrogram. The fluctuation state of the Fourier spectrum is of key importance in distinguishing the distribution of different components in the signal at each frequency. After the Fourier transform of the spectrum is intercepted and reconstructed, the minimum points of the new sequence can separate different components in the signal. Subsequently, the frequency slice function is used to extract each frequency band to obtain better filtering effects than the finite impulse response filter. Finally, the proposed novel correlation spectral negentropy is sensitive to periodic pulses and can be used to screen the component that contains the most fault information. The simulation results show that the proposed Fast Entrogram can effectively extract periodic pulses. It is verified by experimental signals that the method can be applied to fault diagnosis of bearing inner and outer rings.



中文翻译:

一种新颖的快速Entrogram及其在滚动轴承故障诊断中的应用

有效识别滚动轴承的健康状况可以减少旋转机械组件的维护成本。随着各种信号处理理论的发展和完善,从频域提取故障信息的方式已逐渐取代时域方式。作为传统的频谱分段分析方法,快速Kurtogram可以自适应地提取可能包含故障信息的频带以诊断故障。但是,通过快速Kurtogram采用的1/3二叉树滤波器组分割方法获得的中心频率和带宽的帧是固定的。本文提出了一种新的频谱分割方法和频域故障信息的准确过滤方法-快速熵分析法。傅里叶频谱的波动状态对于区分每个频率信号中不同分量的分布至关重要。在频谱的傅立叶变换被截取并重建之后,新序列的最小点可以分离信号中的不同成分。随后,使用频率切片函数来提取每个频带,以获得比有限脉冲响应滤波器更好的滤波效果。最后,所提出的新颖的相关频谱负熵对周期性脉冲敏感,并且可以用于筛选包含最多故障信息的组件。仿真结果表明,所提出的快速Entrogram可以有效地提取周期脉冲。

更新日期:2021-01-07
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