【摘要】 在变负载条件下，基于机器学习的齿轮故障诊断模型面临着依赖特定目标工况样本训练的挑战.为了克服这一局限性，基于齿轮的故障机理，求解了信号中能够反映其健康状态且不随负载变化而改变的特征成分，以此构建了故障频率波形卷积模块，并将其内嵌于卷积神经网络中.此外，为增强网络的特征提取能力，引入多尺度注意力模块.基于上述模块，构建了变负载齿轮故障诊断模型（FWaveNet)，将其应用于东北大学的齿轮故障数据集，结果显示其诊断精度相较于现有模型有显著提升.通过特定的信号处理技术和网络架构设计，在负载波动情况下实现了对齿轮健康状态的精确识别，为变负载齿轮故障诊断的工程应用提供了一种解决方案.更多还原

【Abstract】 Under variable load conditions, machine learning-based gear fault diagnosis models face the challenge of relying on specific target condition samples for training. To overcome this limitation, the feature components in the signal that can reflect the health status of gears and remain invariant to load variations were solved based on the gear fault mechanism, thereby constructing a fault frequency waveform convolution module and embedding it into the convolutional neural network. Additionally, to enhance the network’s feature extraction capability, a multi-scale attention module was introduced. Based on these modules, a variable load gear fault diagnosis model named FWaveNet was constructed and applied to the gear fault dataset from Northeastern University. The results showed that its diagnostic accuracy is significantly better than that of existing models. Through specific signal processing techniques and network architecture design, precise identification of gear health status under load fluctuations is achieved, and a solution for engineering applications in the fault diagnosis of variable load gears is provided.更多还原