College of Mechanical and Vehicle Engineering, Taiyuan University of Technology
Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment
Shanxi Taigang Stainless Steel Co. , Ltd. Cold Rolled Silicon Steel Plant

为了提升采煤机摇臂齿轮故障诊断准确率、减小模型尺寸且方便部署到更多移动端与边缘设备上,搭建了基于轻量化密集连接卷积网络(LW-DenseNet)的采煤机摇臂齿轮故障诊断模型。采用可分离卷积代替传统卷积减少模型参数,提高诊断效率;通过密集连接机制增强特征传播,加强特征提取能力。利用采煤机摇臂加载试验台采集的摇臂齿轮振动信号进行训练并验证模型的有效性。实验结果表明,与多种诊断模型比较,所提方法仅以0.05MB的模型大小即可达到99.276%的分类精度,并利用凯斯西储大学轴承数据集验证了模型具有良好的泛化性。最后对关键层利用t-SNE进行可视化表示,清晰地展现了模型良好的特征提取性能。

In order to improve the accuracy of coal mining machine rocker gear fault diagnosis, reduce the model size and facilitate the deployment to more mobile and edge devices, a lightweight densely connected convolutional network ( LW -DenseNet) based coal mining machine rocker gear fault diagnosis model is built. Separable convolution is used instead of traditional convolution to reduce model parameters and improve diagnosis efficiency; feature propagation is enhanced by dense connection mechanism to strengthen feature extraction capability. The rocker arm gear vibration signals collected from the coal mining machine rocker arm loading test bench are used to train and verify the effectiveness of the model. The experimental results show that the proposed method can achieve 99. 276% classification accuracy with only 0. 05 MB model size compared with various diagnostic models, and the good generalization of the model is verified using the Case Western Reserve University bearing dataset. Finally, the visual representation of the key layer using t - SNE clearly shows the good feature extraction performance of the model.

国家自然科学基金面上项目(52174148);山西省科技合作交流专项(202104041101003);山西省科技重大专项计划“揭榜挂帅”项目(202101010101018);山西省科技创新人才团队项目(202204051001017)

孙晓春, 丁 华, 牛锐祥, 等. 基于 LW-DenseNet 的采煤机摇臂齿轮故障诊断 [J].煤炭工程, 2023, 55(11): 186-192.