【摘要】 行星齿轮箱作为机械系统中常见的减速装置，由于长期在强噪声环境和变工况工作条件下运行，导致采集到的振动信号故障特征微弱、信号模式多变难以识别，针对行星齿轮箱故障诊断效果不佳，泛化能力差的问题，提出一种多尺度时空信息融合驱动的图神经网络故障诊断方法来提高故障诊断模型准确率和泛化能力。该方法首先构建多尺度卷积核对原始时序信号进行不同尺度特征提取，削弱强噪声信号对有效信息的掩盖作用并增强故障特征的表达能力；然后再构造通道注意力机制，根据通道特征重要程度，给不同尺度卷积核提取的特征自适应分配不同权重，对含有关键故障特征的信息片段进行特征强化；最后对卷积输出的多尺度特征，构造空域下的图数据并通过图卷积网络聚合多尺度特征，从而有效利用数据的时序多维信息和空域结构关联信息，实现多尺度下时空域故障信息的深度融合，提高诊断的准确精度和模型的泛化性能。通过利用具有行星齿轮箱结构的风电装备故障数据集对所提方法进行验证，并与其他深度学习方法（第一层宽卷积核深度卷积神经网（WDCNN）、长短时记忆网络（LSTM）、残差网络（ResNet）、多尺度卷积神经网络（MSCNN））进行比较，结果表明：本研究提出的方法在跨负载和跨转速两种工况下的平均诊断准确率分别可以达到98.85%与91.29%，明显优于其他对比方法，验证了本研究提出的方法的强泛化性能和优越性。更多还原

【Abstract】 Due to the long-term operation of planetary gearboxes in strong noise environments and changing working conditions, the collected vibration signals exhibit weak fault characteristics and variable signal patterns,making them difficult to identify. Intelligent fault diagnosis of planetary gearboxes under these conditions remains a challenging task. In order to achieve high diagnostic accuracy and strong model generalization performance, a fault diagnosis method using a graph neural network with a multi-scale time-spatial information fusion mechanism is proposed. The method first uses convolution kernels of different scales to extract features from the original vibration signal, reducing the masking effect of strong noise signals on valuable information and enhancing its feature expression ability. A channel attention mechanism is then constructed to adaptively assign different weights among different channels to features of different scales, enhancing features in segments of information containing crucial fault characteristics. Finally, the multi-scale features of the convolution module output are used to construct graph data with spatial structure information for graph convolution learning. This approach allows for the full utilization and deep fusion of multi-dimensional time domain information and spatial correlation information, effectively improving the accuracy of diagnosis and the generalization performance of the model. The proposed method was verified using a fault dataset of wind power equipment with planetary gearbox structure. The average diagnosis accuracy of the proposed method was found to reach 98. 85% and 91. 29% under cross-load and cross-speed conditions, respectively. These results are superior to other intelligent diagnosis methods, including deep convolutional neural networks with wide first-layer kernels(WDCNN), long short-term memory network(LSTM),residual network(ResNet), and multi-scale convolution neural network(MSCNN). Therefore, the strong generalization performance and superiority of the proposed method were confirmed.更多还原