Stacked Denoising Autoencoder based Fault Diagnosis for Rotating Motor
Tang,Haichuan2; Zhang,Kunting1; Guo,Dingfei1; Jia, Lihao1; Qiao,Hong1; Tian Yin2
Conference NameChinese Control Congress
Conference DateJuly 25, 2018
Conference PlaceWuhan, China
Publication PlaceWuhan, China

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.

Sub direction classification智能机器人
planning direction of the national heavy laboratory高通过性仿生机器人
Paper associated data
Document Type会议论文
Corresponding AuthorJia, Lihao
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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