结合振动特征优选和GWOA-XGBoost的电机轴承故障诊断

为解决电机轴承故障状态难以识别,从而造成诊断精度不高的情况,提出了一种基于信号特征提取与极限梯度提升算法(extreme gradient boosting, XGBoost)结合的电机轴承故障诊断模型。使用优化的变分模态分解获得振动信号的固有模态函数(intrinsic mode function, IMF)分量,再基于多尺度熵理论计算各IMF分量的多尺度熵值进行特征重构。在鲸鱼优化算法(whale optimization algorithm, WOA)中引入遗传算法的选择、交叉、变异操作对WOA进行改进。用改进的WOA算法对XGBoost的超参数进行寻优,获得了帮助XGBoost取得最优分类效果的超参数组合,将7种不同故障类型的振动信号进行重构后输入优化的XGBoost模型进行故障诊断。实验结果表明,所提GWOA-XGBoost模型的电机轴承故障诊断精度能够达到97.14%,相较于传统诊断方法,性能提升效果显著。

Motor bearing fault diagnosis by combining vibration feature optimization and GWOA-XGBoost

In order to solve the problem that the fault state of motor bearings is difficult to identify, which results in low diagnosis accuracy, a motor bearing fault diagnosis model was proposed by combining the signal feature extraction and XGBoost (extreme gradient boosting algorithm). The optimized variational mode decomposition was used to obtain the IMF(intrinsic mode function) components of the vibration signal, and the multi scale entropy value of each IMF component was calculated on the basis of the multi-scale entropy theory for feature reconstruction. The WOA (whale optimization algorithm) was improved by introducing the selection, crossover and mutation operation of genetic algorithm. The improved WOA algorithm was used to optimize the hyperparameters of XGBoost, and obtain the hyperparameter combination that helped the XGBoost achieve the best classification effect. The vibration signals of 7 different types of faults were reconstructed and input into the optimized XGBoost model for fault diagnosis. Experimental results show that the motor bearing fault diagnosis accuracy of the proposed GWOA-XGBoost model can reach 97.14%, which significantly improves the performance compared with the traditional diagnosis method.