基于EWM和SVR的滚动轴承剩余使用寿命预测方法

Remaining useful life prediction method of rolling bearing based on EWM and SVR

为解决滚动轴承有限全寿命监测数据情况下退化特征分布失真导致轴承剩余使用寿命(RUL)预测精度不高的问题,提出一种基于熵权法(EWM)和支持向量回归(SVR)的轴承RUL预测方法。首先,提取振动信号的时域和频域特征,并对特征进行对数变换;然后,通过EWM确定指标权重实现特征选择;最后,采用麻雀搜索算法(SSA)优化SVR模型,以主成分分析(PCA)降维后的低维特征作为优化后的SVR模型的输入,RUL占比作为输出,从而实现轴承剩余寿命的预测。结果表明:在有限监测数据情况下,与其他方法相比,所提方法不但预测性能更加稳定,而且预测的绝对误差平均降低19.51%,均方误差(MSE)平均降低17.73%。

In order to solve the problem that the RUL prediction accuracy of rolling bearings was not high due to the distortion of degradation feature distribution under the condition of limited full-life monitoring data of rolling bearings,a prediction method of the RUL of rolling bearings based on EWM and SVR was proposed.Firstly,the time-domain and frequency-domain features of the vibration signal were extracted,and the logarithmic transformation was performed on the features.Then,the index weights were determined by EWM to realize the feature selection.Finally,SSA was used to optimize the SVR model,and the low-dimensional features after dimensionality reduction by principal component analysis(PCA)were used as the input of the optimized SVR model,and the RUL percentage was used as the output,so as to realize the prediction of the RUL of the bearing.The results show that under the condition of limited monitoring data,compared with other methods,the proposed method not only has a more stable prediction performance,but also has an average reduction of 19.51%in absolute error and 17.73%in mean square error(MSE).