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滚动轴承故障检测深度卷积稀疏自动编码器建模研究

冯玉伯 丁承君 陈雪

冯玉伯, 丁承君, 陈雪. 滚动轴承故障检测深度卷积稀疏自动编码器建模研究[J]. 机械科学与技术, 2018, 37(10): 1566-1572. doi: 10.13433/j.cnki.1003-8728.20180036
引用本文: 冯玉伯, 丁承君, 陈雪. 滚动轴承故障检测深度卷积稀疏自动编码器建模研究[J]. 机械科学与技术, 2018, 37(10): 1566-1572. doi: 10.13433/j.cnki.1003-8728.20180036
Feng Yubo, Ding Chengjun, Chen Xue. Rolling Bearing Fault Detection using Deep Convolution Automatic Sparse Encoder[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(10): 1566-1572. doi: 10.13433/j.cnki.1003-8728.20180036
Citation: Feng Yubo, Ding Chengjun, Chen Xue. Rolling Bearing Fault Detection using Deep Convolution Automatic Sparse Encoder[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(10): 1566-1572. doi: 10.13433/j.cnki.1003-8728.20180036

滚动轴承故障检测深度卷积稀疏自动编码器建模研究

doi: 10.13433/j.cnki.1003-8728.20180036
基金项目: 

天津市科技支撑计划项目(15ZXHLGX00210)、天津市科技支撑计划项目(14ZCDZGX00811)、天津市科技支撑计划项目(13ZCZDGX01200)、天津市产学研合作项目(14ZCZDSF00025)、天津市863成果转化项目(13RCHZGX01116)及天津市863成果转化项目(14RCHZGX00862)资助

详细信息
    作者简介:

    冯玉伯(1974-),高级工程师,博士研究生,研究方向为物联网、机器学习,fyb927@126.com

Rolling Bearing Fault Detection using Deep Convolution Automatic Sparse Encoder

  • 摘要: 针对机械设备故障诊断大多采用有监督学习提取故障特征,而有标签数据难以获取的现状,提出一种在稀疏自动编码器中嵌入卷积网络的深度神经网络。利用希尔伯特和傅里叶变换实现机械设备振动时间序列向Hilbert包络谱的转换,通过卷积网络中多组卷积核自动学习谱空间数据的不同特征,保证了特征提取的自动化、全面性和多样性,稀疏自动编码器搜索具有正交性数据特征的低维表示,并使得编码后的数据具有很强的聚类特性,实现设备的自动故障诊断。通过对滚动轴承振动信号进行分析实验,证明该方法在设备故障诊断中具有去标签化、自动化、鲁棒性等特点。
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出版历程
  • 收稿日期:  2017-11-08
  • 刊出日期:  2018-10-05

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