| 杨潇谊,吴建德,马 军.基于散布熵和余弦欧氏距离的滚动轴承性能退化评估方法[J].电子测量与仪器学报,2020,34(7):15-24 |
| 基于散布熵和余弦欧氏距离的滚动轴承性能退化评估方法 |
| Rolling bearing performance degradation assessment method based on dispersion entropy and cosine Euclidean distance |
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| DOI： |
| 中文关键词: 滚动轴承 性能退化评估 散布熵 余弦欧氏距离 |
| 英文关键词:rolling bearing performance degradation assessment dispersion entropy cosine euclidean distance |
| 基金项目:国家自然科学基金(51765022,61663017)资助项目 |
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| 摘要点击次数: 387 |
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| 中文摘要: |
| 针对传统特征指标评估轴承性能退化状态时可靠性、敏感性低的问题,提出一种基于散布熵和余弦欧氏距离的滚动轴
承性能退化评估方法。 首先,将待测滚动轴承振动信号分为健康数据和测试数据,分别对其进行集成经验模态分解( ensemble
empirical mode decomposition, EEMD),得到若干本征模态分量(intrinsic mode function, IMF),计算各 IMF 分量与原信号的相关
系数,并根据相关系数准则选择 IMF 分量重构信号;然后,计算重构信号的散布熵,通过结合欧氏距离和余弦距离得到健康数
据和测试数据散布熵之间的余弦欧氏距离作为退化指标;最后,利用切比雪夫不等式计算余弦欧氏距离健康阈值,评估轴承性
能退化状态。 实验结果表明,利用散布熵之间的余弦欧氏距离可以有效、及时地判断轴承性能退化状态,并且与其他指标相比,
其敏感性、鲁棒性更高,能够更好地刻画滚动轴承性能退化趋势,为滚动轴承性能退化评估提供新的解决方法。 |
| 英文摘要: |
| Aiming at the problems of low reliability and sensitivity when evaluating the degradation of bearing performance with traditional
characteristic indicators, a method for evaluating the degradation of rolling bearing performance based on dispersion entropy and cosine
Euclidean distance is proposed. First, the vibration signal of the rolling bearing to be tested is divided into health data and test data,
decomposed by EEMD respectively to obtain several Intrinsic Mode Functions (IMF). Calculatethe correlation coefficient between each
IMF component and the original signal, and the IMF components are selected according to the correlation coefficient criterion to
reconstruct signal. Then, the dispersion entropy of the reconstructed signal is calculated, and the Euclidean distance and the cosine
distance are combined to obtain the degradation index cosine Euclidean distance between the health data and the test data dispersion
entropy. Finally, the Chebyshev inequality is used to calculate the cosine Euclidean distance health threshold to evaluate the degradation
of the bearing performance. The experimental result shows that the cosine Euclidean distance between dispersion entropy can effectively
and timely judge the degradation state of the bearing performance, and compared with other indexes, its sensitivity and robustness are
higher, which can better describe the degradation trend of the rolling bearing performance, and provide a new solution for the evaluation
of the rolling bearing performance degradation. |
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