国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 信号处理
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| 标 题： |
A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis
基于降低滚动轴承（REB）基座振动和噪声辅助振动信号分析的滚动轴承故障诊断方法 |
| 作 者： |
Wang, Heng-di
Deng, Si-er
Yang, Jian-xi
Liao, Hui |
| 关键词： |
Rolling element bearing [滚动轴承]
fault diagnosis [ 故障诊断]
empirical mode decomposition [ 经验模态分解]
rolling element bearing foundation vibration [ 滚动轴承基础振动]
noise-assisted vibration signal analysis [ 噪声辅助振动信号分析] |
| 出版年： |
2019 |
| 期刊名： |
Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science
机械工程师学会会刊，C部分。机械工程科学杂志 |
| 年卷期： |
2019, vol.233, no.7 |
| 摘 要： |
Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function's foundation vibration components within +/- 2Xrms are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.
由于滚动轴承原始振动信号中难以提取出初始故障特征，本文基于经验模态分解和峰度准则，提出了一种滚动轴承故障诊断方法，通过降低轴承基频振动并结合噪声辅助振动信号分析。首先，利用经验模态分解将滚动轴承振动信号分解为一组本征模态函数（IMF），并选取具有最大峰度值的本征模态函数分量。随后，对所选的本征模态函数分量进行零均值归一化处理，并去除其在±2Xrms范围内的基频振动分量，以最小化干扰。为消除基频振动分量去除后可能出现的中断和间断现象，向新生成的信号中加入白噪声。然后对加噪后的信号进行经验模态分解，再选择频率带最高的一阶本征模态函数（IMF1），并采用包络分析进行解调。由此得到的包络谱能够显示更显著的故障脉冲特征，对诊断滚动轴承早期故障具有极大帮助。本文提出的算法应用于低噪声REB 6203的故障诊断，测试结果表明该方法能够准确、快速地识别滚动轴承的早期故障。 |
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