文献类型：期刊文献

中文题名：基于改进型深度网络数据融合的滚动轴承故障识别

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

作者：冯新扬[1];张巧荣[1];李庆勇[1,2,3]

第一作者：冯新扬

机构：[1]河南财经政法大学计算机与信息工程学院;[2]山东大学青岛校区公共(创新)实验教学中心;[3]山东大学信息科学与工程学院

第一机构：河南财经政法大学计算机与信息工程学院

年份：2019

卷号：0

期号：2

起止页码：52-62

中文期刊名：重庆大学学报：自然科学版

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家重大科学仪器设备开发专项项目(2012YQ20022407);河南省科技攻关项目(122102210387);河南省教育厅科技攻关项目(13B52090)~~

语种：中文

中文关键词：深度学习;卷积神经网络;故障识别;振动信号;滚动轴承;特征提取

外文关键词：deep learning;convolutional neural network;fault identification;vibration signal;rolling bearing;feature extraction

摘要：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。
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 achieveaccurate 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.