基于温振融合与深度自编码器的高速动车组轴箱轴承故障诊断模型

High-speed EMU Axle Box Bearing Fault Diagnosis Model Based on Thermal-vibration Fusion and Deep Auto-encoder

因物理监测信息利用不足,动车组轴箱轴承故障诊断存在准确率较低问题。首先,利用高速动车组轴箱轴承试验台获取丰富数据,融合温度特征数据与振动特征数据,并使用主成分分析法进行融合与降维;然后,建立基于温振融合与DAE(深度自编码器)的轴箱轴承故障诊断模型,并通过深度自编码器进行模型训练;最后,用高速动车组轴箱轴承试验台测试集的数据进行模型验证。验证结果表明:与其他对比模型相比,基于温振融合与DAE的轴箱轴承故障诊断模型的诊断准确率更高。

Because of the insufficient use of physical monitor-ing information,the fault diagnosis of EMU(electric multiple unit)axle box bearing has a low accuracy rate problem.First,high-speed EMU axle box bearing test bench is used to obtain ample data,integrate temperature and vibration feature data,and the PCA(principal component analysis)method is used for fusion and dimension reduction.Then,the axle box bearing fault diagnosis model based on temperature-vibration fusion and DAE(deep auto-encoder)is established,and the model was trained by the DAE.Finally,the model is verified by the test set data obtained from the high-speed EMU axle box bearing test bench.Verification results show that,compared with other comparative models,the axle box bearing fault diagnosis model based on temperature-vibration fusion and DAE has higher accuracy rate.