A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis
A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis
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DOI：
10.3390/s19183994
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发表时间：
2019-09
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影响因子：
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通讯作者：
D. Zhen;Junchao Guo;Yuandong Xu;Hao Zhang;F. Gu
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文献类型：
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作者：
D. Zhen;Junchao Guo;Yuandong Xu;Hao Zhang;F. Gu
为了实现滚动轴承故障的精确检测，提出了一种基于加权平均系综经验模态分解（WAEEMD）和调制信号双谱（MSB）的非平稳振动信号分析的故障检测方法。双谱是一种三阶统计量，不仅能有效抑制高斯噪声，而且有助于识别相位耦合。但是，它不能有效地分解振动信号中固有的调制分量。为了缓解这一问题，基于信号调制特性的MSB被开发用于解调和降噪。然而，直接应用MSB在非平稳信号中提取故障特征时存在一定的干扰频率成分。集成经验模态分解（EEMD）是一种先进的非线性非平稳信号处理方法，它可以将信号分解成一系列平稳的内模态函数（IMFs）。该方法利用WAEEMD和MSB进行基于振动特征分析的轴承故障诊断。首先，利用EEMD将振动信号分解成不同频带的imf；然后，基于Teager能量峰度（TEK），利用加权平均方法（WAEEMD）将imf重构为一个新的信号。最后，利用MSB对重构信号中的调制分量进行分解，提取故障特征频率进行故障检测。通过对电机轴承外圈故障和齿轮箱轴承内圈故障的诊断，验证了该方法的有效性和性能。实验结果表明，WAEEMD-MSB在故障特征提取方面优于传统的MSB和EEMD-MSB，在滚动体轴承故障检测中具有精确有效的优势。
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.