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首页> 外文期刊>Journal of Failure Analysis and Prevention >Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy
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Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy

机译：基于LCD相对光谱熵的滚动轴承退化状态识别

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摘要

In the interest of obtaining an effective bearing degradation feature from complex, nonlinear, and nonstationary vibration signals, a new analytical methodology based on local characteristic-scale decomposition (LCD) and relative entropy theory is proposed. On the one hand, LCD is a new and relatively excellent time-frequency analysis method to analyze practical vibration signals polluted by noise. On the other hand, relative entropy theory is a good way to characterize different degradation states by calculating the probability distribution difference between the degradation signals and the normal signal. Combining the above two theories, two new degradation features named LRNE and LRQE are extracted to indicate the bearing degradation trend from normal state to even failure state. The noise resistance ability and extensive applicability of both the features are verified by simulation signal. For further analysis of experimental vibration signals, the two features have a satisfying performance to characterize different bearing degradation states. With the help of gray relational analysis and fuzzy C-means clustering, the proposed two characteristics can identify different bearing degradation states of inner ring fault mode with high accuracy. In the end, the two features are applied to doing bearing failure analysis with the full-life bearing data. The results show that the LRNE and LRQE are sensitive to bearing degradation trend in the whole life of bearing.
机译：为了从复杂的，非线性的和非平稳的振动信号中获得有效的轴承退化特征，提出了一种基于局部特征尺度分解（LCD）和相对熵理论的新分析方法。一方面，LCD是一种新的且相对出色的时频分析方法，用于分析受噪声污染的实际振动信号。另一方面，相对熵理论是通过计算退化信号与正常信号之间的概率分布差异来表征不同退化状态的好方法。结合以上两个理论，提取了两个新的退化特征LRNE和LRQE，以指示轴承从正常状态到均匀破坏状态的退化趋势。通过仿真信号验证了这两个功能的抗噪声能力和广泛的适用性。为了进一步分析实验振动信号，这两个功能具有令人满意的性能来表征不同的轴承退化状态。借助灰色关联分析和模糊C均值聚类，提出的两个特征可以高精度地识别内圈故障模式的不同轴承退化状态。最后，将这两个功能应用于使用轴承全寿命数据进行轴承故障分析。结果表明，LRNE和LRQE在轴承的整个寿命中对轴承的退化趋势敏感。

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