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Time and frequency domain scanning fault diagnosis method based on spectral negentropy and its application

机译:基于光谱预期的时间和频域扫描故障诊断方法及其应用

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Rolling bearings are one of the most important components in rotating machinery. It is important to accurately determine the center frequency and bandwidth of the resonant frequency band for bearing fault diagnosis. There are two problems with the existing methods for extracting bearing fault characteristics. First, due to the unreasonable spectrum segmentation, the determined resonant frequency band contains only partial fault information or hidden irrelevant information. Finally, because of the interference of the accidental impact, the correct fault characteristic information cannot be extracted. To solve the above problems, a time-frequency domain scanning empirical spectral negentropy method (T-FSESNE) based on spectral negentropy (NE) and empirical wavelet transform (EWT) is proposed in this paper. The signal is filtered twice by EWT filter: Firstly, the central frequencies of all resonance side bands are determined by using frequency-domain spectral negentropy, and then the optimal bandwidth of the resonance side bands is determined by using time-domain spectral negentropy. According to the determined center frequency and bandwidth, each component is extracted and analyzed by envelope spectrum to realize bearing fault diagnosis. The validity of the extracted methods is verified by bearing fault simulation and experimental signals. The results show that not only the interference of accidental impact can be effectively avoided but also the optimal center frequency and bandwidth can be determined quickly and accurately. More importantly, this method can determine the position of multiple resonance sidebands, which is more suitable for the analysis of complex fault vibration signals in rolling bearings.
机译:滚动轴承是旋转机械中最重要的部件之一。重要的是准确地确定轴承故障诊断的谐振频带的中心频率和带宽。提取轴承故障特性的现有方法存在两个问题。首先,由于不合理的频谱分割,所确定的谐振频带仅包含部分故障信息或隐藏的无关信息。最后,由于意外影响的干扰,无法提取正确的故障特征信息。为了解决上述问题,基于频谱负熵(NE)和经验小波的时间 - 频率域扫描经验频谱负熵法(T-FSESNE)变换(EWT)在本文提出。通过EWT滤波器进行两次滤波信号:首先,通过使用频域光谱成对确定所有谐振侧频带的中心频率,然后通过使用时域光谱预期确定谐振侧带的最佳带宽。根据所确定的中心频率和带宽,通过包络谱提取和分析每个组件,以实现轴承故障诊断。通过轴承故障仿真和实验信号验证提取的方法的有效性。结果表明,不仅可以有效地避免意外冲击的干扰,而且可以快速准确地确定最佳中心频率和带宽。更重要的是,该方法可以确定多个谐振边带的位置,这更适合于分析滚动轴承中的复杂故障振动信号。

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