基于 VMD 相对能量熵和自适应 ARMA模型的轴承性能退化趋势动态预警

为了有效监测滚动轴承性能退化趋势及其指标异常波动,提出了一种基于变分模态分解(VMD)的相对能量熵和自回 归滑动平均(ARMA)模型的滚动轴承性能退化趋势动态预警方法。 方法利用 VMD 对滚动轴承寿命数据进行分解,得到有限带 宽固有模态函数(BLIMFs);对该 BLIMFs 分量的能量进行相对熵分析,提取滚动轴承性能退化特征,得到 VMD 相对能量熵的轴 承性能退化评估指标;该相对能量熵值作为输入供 ARMA 模型进行动态回归预测。 试验结果表明,该方法能有效监测滚动轴 承性能退化趋势、指标的异常波动,验证了所提方法的有效性。

Dynamic prediction of bearing performance degradation trend based on VMD relative energy entropy and adaptive ARMA model

In order to effectively monitor the rolling bearing performance degradation trend and its abnormal fluctuations, a dynamic early warning method of rolling bearing performance degradation trend based on the relative energy entropy of variational mode decomposition (VMD) and the adaptive ARMA model is proposed. Methods VMD was used to decompose the life data of rolling bearing to obtain band-limited intrinsic mode functions (BLIMFs). The energy of the BLIMFs component is analyzed by relative entropy, and the characteristics of rolling bearing performance degradation are extracted to obtain the bearing performance degradation evaluation index of VMD relative energy entropy. The energy entropy value extracted by VMD decomposition is used as an input for ARMA model for dynamic regression prediction. The test results show that this method can effectively monitor the degradation trend of rolling bearing performance and the abnormal fluctuation of indexes, and verify the effectiveness of the proposed method.