基于改进型深度网络数据融合的滚动轴承故障识别

针对传统智能诊断方法依赖于信号处理和故障诊断经验提取故障特征以及模型泛化能力差的问题,基于深度学习理论,提出将卷积神经网络算法结合softmax分类器,针对数据集不平衡问题引入加权损失函数、正则化以及批量归一化等模型优化技术搭建适于滚动轴承故障诊断的改进型深度卷积神经网络模型。模型从原始实测轴承振动信号出发逐层学习实现特征提取与目标分类。实验结果表明,优化后的深度学习模型可实现对早期微弱故障、不同程度故障的精确识别,在不平衡数据集上也可达到95%的识别准确率,并且模型拥有较快的收敛速度和较强的泛化能力。

Fault recognition of rolling bearing based on improved deep networks with data fusion in unbalanced data sets

Traditional intelligent diagnosis methods rely too much on the experience of signal processing and fault diagnosis to extract fault features, and generalization ability of models is poor. Based on the theory of deep learning, a convolutional neural network algorithm combined with the softmax classifier is proposed to introduce weighting to the solution of data set imbalance problem. Model optimization techniques such as weighted loss function, regularization, and batch normalization are applied to the construction of an improved deep convolutional neural network model for rolling bearing fault diagnosis. The model learns from the original measured bearing vibration signal by layer-by-layer learning to achieve feature extraction and target classification. Experimental results show that the optimized deep learning model can achieve accurate recognition of early weak faults and different levels of faults, and its recognition accuracy on unbalanced data sets can reach 95%. Furthermore the model has faster convergence speed and strong generalization ability.