Abstract Gear pitting fault diagnosis has always been an important subject to industry and research community. In the past, the diagnosis of early gear pitting faults has usually been carried out under single gear health state. In order to diagnose the early gear pitting faults with mixed operating conditions and reduce the number of training parameters, a new method is proposed in this paper. The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions. Compared to the traditional convolutional neural network, the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters. The residual connection can solve the representational bottleneck problem of the features in the model. Moreover, the method proposed in this paper applies the search algorithm to select better hyperparameters of the model. The raw vibration signals of the gear pitting faults at different speeds collected in a gear test rig are used to validate the effectiveness of the proposed method. The results show that the proposed method can accurately diagnose the early gear pitting faults with mixed speeds. In comparison with other machine learning models, the proposed method has provided a better diagnostic accuracy with fewer model parameters.

中文翻译：

---

基于残差连接自适应一维可分离卷积的混合工况齿轮点蚀故障诊断

摘要 齿轮点蚀故障诊断一直是工业界和研究界的重要课题。过去，早期齿轮点蚀故障的诊断通常是在单个齿轮健康状态下进行的。为了诊断混合工况下的早期齿轮点蚀故障并减少训练参数的数量，本文提出了一种新的方法。所提出的方法使用具有残差连接网络的自适应一维可分离卷积来对混合操作条件下的齿轮点蚀故障进行分类。与传统的卷积神经网络相比，带有残差连接网络的可分离卷积可以进行逐点卷积的通道卷积，有效减少网络参数的数量。残差连接可以解决模型中特征的表征瓶颈问题。此外，本文提出的方法应用搜索算法来选择模型的更好超参数。在齿轮试验台上收集的不同速度下齿轮点蚀故障的原始振动信号用于验证所提出方法的有效性。结果表明，所提方法能够准确诊断出早期混速齿轮点蚀故障。与其他机器学习模型相比，所提出的方法以更少的模型参数提供了更好的诊断准确性。在齿轮试验台上收集的不同速度下齿轮点蚀故障的原始振动信号用于验证所提出方法的有效性。结果表明，所提方法能够准确诊断出早期混速齿轮点蚀故障。与其他机器学习模型相比，所提出的方法以更少的模型参数提供了更好的诊断准确性。在齿轮试验台上收集的不同速度下齿轮点蚀故障的原始振动信号用于验证所提出方法的有效性。结果表明，所提方法能够准确诊断出早期混速齿轮点蚀故障。与其他机器学习模型相比，所提出的方法以更少的模型参数提供了更好的诊断准确性。