基于 KL-VMD 和综合特征指标的 滚动轴承性能退化评估

针对滚动轴承早期性能退化点难以检测的问题，提出一种结合改进变分模态分解(VMD)和综合特征指标的滚动轴承性能退化评估方法。首先使用K-L散度优化VMD的参数，用优化后的VMD对轴承振动信号进行分解，通过Wasserstein距离筛选对退化特征较为敏感的模态分量，对其奇异值分解得到奇异值特征；结合信号的熵能比和置信值组成滚动轴承退化综合特征指标，最后构建支持向量数据描述模型计算性能退化指标，实现滚动轴承的早期微弱故障检测及性能退化评估。利用轴承全寿命实验数据验证方法的有效性，对早期性能退化点的检测结果相较于其他退化评估方法有所提前，为滚动轴承性能退化评估提供新思路。

Performance degradation assessment of rolling bearing based on KL-VMD and comprehensive characteristic indexes

In view of the problem that the early performance degradation point of rolling bearing is difficult to monitor, a method based on improved VMD and comprehensive characteristic indexes performance degradation assessment was proposed. Firstly, the parameters of VMD were optimized by Kullback Leibler divergence (KL-divergence), the bearing vibration signals were decomposed by the optimized VMD, and the modal components that sensitive to degradation characteristics were screened by wasserstein distance ( WD) method. Then singular value decomposition ( SVD) was performed to obtain singular value characteristics. Secondly, the comprehensive characteristic index of rolling bearing degradation was composed by combining the entropy energy rate ( EER) and confidence value (CV). Finally, SVDD model was used to calculate performance degradation index to realize early weak fault detection and performance degradation assessment. The validity of the proposed method was verified by using the bearing life cycle experiment data, the detection results of early performance degradation points are earlier than other degradation assessment methods, which provides a new idea for performance degradation assessment of rolling bearings.