摘要
通过分析轴承运转情况,提出了一维空洞卷积模型用于轴承故障诊断。此模型可以直接以原始的信号数据输入模型,自动完成特征提取和故障诊断以及故障分类。实验结果表明,一维空洞卷积神经网络可以很好地应用到机械故障诊断领域,此模型可以很好地完成对轴承的故障识别与诊断。
A one-dimensional dilated convolution model was proposed for fault diagnosis of bearing by analyzing the operation of the bearing.This model can directly input the original signal data into the model,and automatically complete feature extraction,fault diagnosis and fault classification.Experiment results show that the one-dimensional dilated convolution neural network can be well applied to the field of mechanical fault diagnosis and this model can well complete the fault identification and diagnosis of bearing.
作者
何宗博
杨喜旺
He Zongbo;Yang Xiwang(North University of China,Taiyuan 030051,China)
出处
《煤矿机械》
北大核心
2020年第12期150-153,共4页
Coal Mine Machinery
基金
山西省自然科学基金(201901D111157)
山西省重点研发计划(201903D421008)。
关键词
一维空洞卷积
故障诊断
故障分类
自动特征提取
one-dimensional dilated convolution
fault diagnosis
fault classification
automatic feature extraction
