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基于深度残差网络的采煤机摇臂齿轮故障诊断
Fault diagnosis of shearer rocker gear based on deep residual network
【索引】李长文,程泽银,张小刚,等.基于深度残差网络的采煤机摇臂齿轮故障诊断[J].工矿自动化,2021,47(3):71-78.
【Reference】LI Changwen,CHENG Zeyin,ZHANG Xiaogang,et al.Fault diagnosis of shearer rocker gear based on deep residual network[J].Industry and Mine Automation,2021,47(3):71-78.
【DOI】10.13272/j.issn.1671-251x.2020110043
【作者】李长文1,程泽银2,3,张小刚2,3,丁华2,3
【Author】 LI Changwen1,CHENG Zeyin2,3,ZHANG Xiaogang2,3,DING Hua2,3
【作者机构】1.山西金融职业学院 信息技术系, 山西 太原030008;2.太原理工大学 机械与运载工程学院, 山西 太原030024;3.煤矿综采装备山西省重点实验室, 山西 太原030024)
【Unit】1.Department of Information Technology, Shanxi Professional College of Finance, Taiyuan 030008, China; 2.College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China; 3.Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, Taiyuan 030024, China
【摘要】针对传统的采煤机摇臂齿轮故障诊断方法不能自主提取特征,导致齿轮故障诊断精度和效率不佳等问题,构建了基于深度残差网络(ResNet)的采煤机摇臂齿轮故障诊断模型。通过预激活残差单元模块降低模型的复杂度,使模型收敛速度更快;通过对振动信号进行数据重组,优化数据输入方式,提高模型对采煤机摇臂齿轮故障的识别能力。在采煤机摇臂加载实验台上进行模型验证实验,采集摇臂直齿轮正常、磨损、断裂、点蚀和裂纹5种状态下的振动信号,得出其特征具有明显差异;对测试集的混淆矩阵进行可视化分析,验证了ResNet模型能够很好地实现采煤机摇臂齿轮故障分类;与DNN模型和LeNet-5模型对比结果表明,ResNet模型具有更高的故障诊断精度和效率,综合识别率和F-score分别达到99.19%和99.05%;采用t-SNE技术对ResNet模型的最大池化层、预激活残差单元模块和全连接层输出的高维特征进行降维和可视化,验证了ResNet模型具有较强的特征提取能力。
【Abstract】The traditional shearer rocker gear fault diagnosis methods cannot extract features autonomously, resulting in poor gear fault diagnosis accuracy and efficiency. In order to solve the above problems, a fault diagnosis model of shearer rocker gear based on deep residual network (ResNet) is constructed. By pre-activating the residual unit module, the complexity of the model is reduced so as to make the model converge faster. By reorganizing the vibration signal data, the data input method is optimized so as to improve the model's ability to identify the fault of shearer rocker gear. Model verification tests are carried out on the rocker gear loading test bench of shearer to collect vibration signals of the rocker spur gears under five states of normal, worn, fractured, pitting and cracked. It is concluded that there are significant differences in their characteristics. The visual analysis of the confusion matrix of the test set verifies that the ResNet model can realizeshearer rocker gear fault classification well. Moreover, the comparison results with the DNN model and LeNet-5 model show that the ResNet model has higher fault diagnosis accuracy and efficiency. The comprehensive recognition rate and F-score reach 99.19% and 99.05% respectively. The t-SNE technology is used to reducedimension and visualize the high-dimensional features output from the maximum pooling layer, the pre-activated residual unit module and the fully connected layer of the ResNet model, which verifies that the ResNet model has strong feature extraction capability.
【关键词】 采煤机摇臂; 齿轮; 故障诊断; 深度残差网络; 深度学习
【Keywords】shearer rocker; gear; fault diagnosis; deep residual network; deep learning
【文献出处】工矿自动化,2021年3期
【基金】国家自然科学基金专项项目(J1924025);山西省重点研发项目(201903D121064);山西省科技基础条件平台项目(201805D141002)
【分类号】TD632.2
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