结合CEEMDAN和灰度关联分析方法的滚动轴承性能退化评估

Performance Degradation Evaluation of Rolling Bearing Based on CEEMDAN and Gray Correlation Analysis

针对大型机械设备中滚动轴承容易发生故障的问题,提出一种将自适应噪声的完备经验模态分解(CEEMDAN)和灰度关联分析相结合的滚动轴承性能退化评估方法。首先利用CEEMDAN对轴承全寿命周期的振动信号进行分解,得到能量熵特征,其次以正常状态下的特征矢量作为灰度关联分析的参考序列,然后计算轴承全寿命周期内的特征矢量与正常特征矢量的关联度,作为性能退化过程的定量评估指标,结果表明该方法能及时发现早期故障,并能很好的描述轴承退化的各个阶段。最后利用基于CEEMDAN和Hilbert包络解调的方法对评估结果的正确性进行了验证。

Aiming at the problem that rolling bearings are prone to failure in large mechanical equipment, an evaluation method for rolling bearings' performance degradation is proposed, which combines the complete empirical mode decomposition (CEEMDAN) of adaptive noise with the grey correlation analysis. Firstly, by using CEEMDAN to decompose the vibration signal of the whole life time of bearing, the energy entropy feature was obtained. Then, by taking the characteristic of the normal vector as reference variables of gray correlation analysis, the correlation degree was calculated between the feature vector and the normal vector of the bearing’s whole life time as a quantitative evaluation index in the process of performance degradation. The results show that the method can figure out early failure and describe the bearing degradation at each stage. Finally, CEEMDAN and Hilbert envelope demodulation were used to verify the validity of the evaluation results.