【摘要】 为解决现有滚动轴承故障诊断方法无法兼顾高准确率与实时性的问题，提出了一种基于小波时频图与MobileViT的滚动轴承故障诊断方法。该方法通过连续小波变换将原始信号表示为小波时频图，然后将小波时频图作为特征图输入到MobileViT进行训练，实现滚动轴承故障状态识别。实验结果表明，与对比方法相比，该方法在公开数据集中的故障诊断率达到100%,且具有更少的参数量与计算量，具有工程应用价值。更多还原

【Abstract】 In order to solve the problem that the existing fault diagnosis methods of rolling bearings cannot take into account both high accuracy and real-time, a fault diagnosis method of rolling bearings based on wavelet time-frequency graph and MobileViT is proposed. In this method, the original signal is represented as a wavelet time-frequency graph by continuous wavelet transform, and then the wavelet time-frequency graph is input to MobileViT as a feature graph for training, and the fault state recognition of rolling bearings is realized. The experimental results show that, compared with the comparison method, the proposed method has a fault diagnosis rate of 100% in the open data set and less parameters and calculation, so it has engineering application value.更多还原