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Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis

机译：基于经验小波变换的轴承故障诊断应用增强的快速Kurtogram应用

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摘要

The fast kurtogram (FK), as a fast and effective method for fault diagnosis, is well accepted by many experts and scholars. However, the FK can only estimate the bandwidth and central frequency which come from resonance modulation of the signal. Sometimes useful information (containing faults) may be lost due to the inaccuracy of the estimated center frequency or bandwidth. In this paper, a novel method named empirical scanning spectrum kurtosis (ESSK), based on empirical wavelet transform (EWT), is proposed. Constructed by the principle of EWT, a set of filters with varying bandwidth scan and filter the whole frequency domain from low to high and a series of empirical modal components are obtained. Then, the spectral kurtosis (SK) of these components is calculated. The center frequency and bandwidth corresponding to the component which has the maximum SK are selected as the optimal center frequency and bandwidth. This method can adaptively and accurately find the frequency band containing rich fault feature information, and extract the corresponding component. Multiple simulation signals and experimental signals are used to verify the effectiveness of the proposed method. The results show that the method can maximally extract the components which contain the periodic pulse information and accurately diagnose the faults of the rolling bearing. In addition, comparisons with three popular signal processing methods, including the sparsogram, fir-based FK and short-time Fourier transform (STFT)based FK are conducted to highlight the superiority of the proposed method.
机译：快速KurtoGram（FK），作为一种快速有效的故障诊断方法，很多专家和学者都接受了很好的接受。然而，FK只能估计来自信号的谐振调制的带宽和中心频率。由于估计的中心频率或带宽的不准确性，有时有用的信息（包含故障）可能会丢失。本文提出了一种基于经验小波变换（EWT）的经验扫描光谱峰值（ESSK）的新方法。通过EWT的原理构造，一组具有不同带宽扫描的滤波器和从低电平到高电平的整个频域滤波，并获得一系列经验模态分量。然后，计算这些组分的光谱峰峰（SK）。选择具有最大SK的组件的中心频率和带宽被选为最佳中心频率和带宽。该方法可以自适应，准确地找到包含丰富故障特征信息的频带，并提取相应的组件。多种仿真信号和实验信号用于验证所提出的方法的有效性。结果表明，该方法可以最大地提取包含周期性脉冲信息的组件，精确地诊断滚动轴承的故障。另外，进行了三种流行信号处理方法的比较，包括SparsoGram，基于FIR基的FK和基于短时傅立叶变换（STFT）的FK，以突出提出的方法的优越性。

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