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稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用

汤芳 刘义伦 龙慧

汤芳, 刘义伦, 龙慧. 稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用[J]. 机械科学与技术, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304
引用本文: 汤芳, 刘义伦, 龙慧. 稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用[J]. 机械科学与技术, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304
Tang Fang, Liu Yilun, Long Hui. Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304
Citation: Tang Fang, Liu Yilun, Long Hui. Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 352-357. doi: 10.13433/j.cnki.1003-8728.2018.0304

稀疏自编码深度神经网络及其在滚动轴承故障诊断中的应用

doi: 10.13433/j.cnki.1003-8728.2018.0304
基金项目: 

国家自然科学基金项目(51375500,61402167)与湖南科技大学机械设备健康维护湖南省重点实验室开放基金项目(201605)资助

详细信息
    作者简介:

    汤芳(1987-),硕士研究生,研究方向为信号处理与机械故障诊断,thstn@126.com

    通讯作者:

    刘义伦,教授,博士,ylliu@csu.edu.cn

Application of Deep Neural Network with Sparse Auto-encoder in Rolling Bearing Fault Diagnosis

  • 摘要: 针对目前滚动轴承故障诊断主要采用监督式学习提取故障特征的现状,提出了一种基于稀疏自编码的深度神经网络,实现非监督学习自动提取滚动轴承振动信号的内在特征用于滚动轴承故障诊断。首先,将轴承故障振动信号的频谱训练稀疏自编码获得参数;然后用稀疏自编码获得的参数和轴承振动信号频谱的频谱训练深度神经网络,并结合反向传播算法对深度神经网络进行整体微调提高分类准确度;最后用训练好的深度神经网络来识别滚动轴承故障。对正常轴承、外圈点蚀故障、内圈点蚀故障和滚动体裂纹故障振动信号的分析结果表明:相比反向传播神经网络,提出的深度神经网络更能准确的识别滚动轴承故障类型。
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出版历程
  • 收稿日期:  2016-12-13
  • 刊出日期:  2018-03-05

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