Stacked Denoising Autoencoder based Fault Diagnosis for Rotating Motor
Tang,Haichuan2; Zhang,Kunting1; Guo,Dingfei1; Jia, Lihao1; Qiao,Hong1; Tian Yin2
2018
会议名称Chinese Control Congress
会议日期July 25, 2018
会议地点Wuhan, China
出版地Wuhan, China
出版者IEEE
摘要

Fault diagnosis is vital for normal operation of the rotating motor. An effective and reliable deep learning method
known as stacked denoising autoencoder (SDAE) is investigated in this paper, which can extract the features from the pending
signals with disturbances. Deep adaptive networks are designed to extract features automatically from time domain data and
frequency domain data of motor vibration signal, respectively. Then, the network parameters of the SDAE are trained to
reconstruct the signal features, and clustering results are investigated. Finally, a classification layer is added to the top layer of the
SDAE network for the fault isolation. It is shown that, the diagnosis accuracy with input of vibratory frequency signal is higher
than that of time domain signal. The features extracted by SDAE can represent complex mapping relationships between signal
and various running status, and the accuracy is improved comparing with traditional fault diagnosis methods.

语种英语
七大方向——子方向分类智能机器人
国重实验室规划方向分类高通过性仿生机器人
是否有论文关联数据集需要存交
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/57438
专题多模态人工智能系统全国重点实验室_仿生进化机器人
通讯作者Jia, Lihao
作者单位1.中国科学院自动化研究所
2.中车研究院
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Tang,Haichuan,Zhang,Kunting,Guo,Dingfei,et al. Stacked Denoising Autoencoder based Fault Diagnosis for Rotating Motor[C]. Wuhan, China:IEEE,2018.
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