【摘要】 为了克服干气密封运行中端面接触状态参数(膜厚、端面开启时间)测量困难的问题，提出自注意力机制融合稠密连接网络(DenseNet-convolutional block attention module, DenseNet-CBAM)的干气密封端面摩擦润滑状态识别方法。根据斯特里贝克曲线和干气密封运行规律分析端面可能出现的摩擦润滑状态：流体润滑，边界润滑、混合润滑。通过声发射传感器采集密封系统运行时的声发射信号，通过滤波、时域分析、频域分析得出能够表征各种摩擦润滑状态的特征分量，获取三维连续小波(3D continuous wavelet transform, 3D-CWT)时频图，最终基于深度学习模型Densenet-CBAM识别时频图，实现密封系统摩擦润滑状态识别。与其他二维时频特征图作为输入端相比，3D-CWT时频图提高了状态识别的准确率。同时，相较于其他深度学习模型，该方法对干气密封摩擦润滑状态识别精度高，达到了99.27%。更多还原

【Abstract】 In order to solve the problem that it is difficult to measure key contact state parameters(such as membrane thickness and open time of end faces) during the operation of dry gas seals, a method for recognizing the friction lubrication states of dry gas seal end faces was proposed by integrating a self-attention mechanism with a DenseNet-CBAM(convolutional block attention module) network. Based on the Stribeck curve and the operating laws of dry gas seals, the potential friction lubrication states of the end faces were identified as fluid, boundary and mixed lubrication. Acoustic emission signals generated during the operation of the seal system were collected by using sensors, and characteristic components representing various friction lubrication states were extracted through filtering, time-domain and frequency-domain analysis. A 3D continuous wavelet transform(3D-CWT) was applied to generate time-frequency maps, and ultimately, the deep learning model DenseNet-CBAM was employed to recognize these maps and identify the friction lubrication states of the seal system.The results show that the 3D-CWT time-frequency map improves the accuracy of state identification compared to other 2D time-frequency feature maps as inputs.Compared to other deep learning models, this approach achieves higher accuracy in identifying the friction lubrication state of dry gas seals, reaching 99.27%.更多还原