【摘要】 在对矿山机械装备中使用的轴承进行故障诊断时，易受噪声干扰及多变工况的影响，同时也难以适应不同诊断任务，针对这一系列问题，提出了一种基于分支卷积神经网络(B-CNN)的托辊轴承故障分级诊断方法。首先，根据具体的诊断任务故障的层级结构进行了划分，采用多层标签表示健康状态、故障类型和损伤程度；通过交替卷积和池化层，构建了一维卷积神经网络(1DCNN)特征提取块；然后，将层级结构和特征提取块融合，设计出了一种基于分支一维卷积神经网络(B-1DCNN)的轴承故障分级诊断模型；最后，使用美国凯斯西储大学轴承数据和自建的带式输送机托辊故障模拟实验台数据，对托辊轴承故障进行了模拟实验，对该方法在噪声干扰和多变工况下的诊断性能进行了验证。研究结果表明：该方法成功实现了对托辊轴承故障从粗到精的分级诊断，对噪声干扰和变工况具有较好的鲁棒性，且与支持向量机(SVM)和反向传播神经网络(BPNN)模型相比，该方法的故障诊断性能更好。更多还原

【Abstract】 Aiming at the problems that bearing fault diagnosis in mining machinery was easily affected by noise interference and variable working conditions, and was difficult to adapt to different diagnosis tasks, a branch convolutional neural network(B-CNN) hierarchical diagnosis method for idler bearing fault was proposed. Firstly, the hierarchical structure of faults was divided according to the specific diagnostic tasks, and multi-level labels were used to represent health status, fault types and degrees of damage. One-dimension convolutional neural network(1 DCNN) feature extraction blocks were constructed by alternating convolutional and pooling layers. Then, a bearing fault hierarchical diagnosis method model based on branch one-dimension convolutional neural network(B-1 DCNN) was designed by combining the hierarchical structure with the feature extraction blocks. Finally, the simulated idler bearing fault experiment was conducted through using the data from the Case Western Reserve University bearing and self-built belt conveyor idler fault test bed, and the diagnostic performance of the method under noise interference and variable working conditions was verified. The results show that the proposed method successfully achieves the diagnosis of roller bearing fault from coarse to fine, and it exhibits good robustness to noise interference and variable working conditions. Comparing with support vector machine(SVM) and back propagation neural network(BPNN) models, this method has better performance in fault diagnosis.更多还原