故障诊断领域中常将信号处理与深度学习相结合以实现更好的诊断效果。基于此，对辛几何模态分解与ResNeXt神经网络分别进行了改进与优化，提出了一种基于优化辛几何模态分解与改进ResNeXt神经网络相结合的齿轮箱故障诊断模型。首先将采集到的振动信号经优化辛几何模态分解进行筛选重构，得到有效分量，之后送入改进ResNeXt神经网络进行故障的识别分类。通过使用渥太华大学滚动轴承变工况数据，验证了模型的可行性；通过使用动力传动故障诊断综合实验台（drivetrain dynamics simula, DDS）齿轮箱数据进行对比实验与抗噪性实验，验证了改动的有效性与模型的泛化性。

Signal processing and deep learning are often combined to achieve better diagnostic results in the field of fault diagnosis. Based on this, the symplectic geometric mode decomposition was improved and the ResNeXt neural network was optimized, and then a gearbox fault diagnosis model was proposed based on the combination of optimized symplectic geometric mode decomposition and ResNeXt neural network was improved. Firstly, the collected vibration signals were filtered and reconstructed by optimized symplectic geometric mode decomposition to obtain the effective components. Then it was sent to the improved ResNeXt neural network for fault recognition and classification. The rolling bearing variable condition data from the University of Ottawa was used to verify the feasibility of the model. The gearbox data from drivetrain dynamics simula (DDS) was used for contrast experiment and anti-noise experiment, which verified the effectiveness of changes and the generalization of the model.