TY - GEN
T1 - Unsupervised Feature Learning of Gearbox Fault Using Stacked Wavelet Auto-encoder
AU - Shao, Haidong
AU - Jiang, Hongkai
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - Unsupervised feature learning and automatic fault diagnosis of gearbox is still a major challenge. For this purpose, a novel method called stacked wavelet auto-encoder (SWAE) is proposed in this paper. Firstly, wavelet function is adopted as the nonlinear activation function to design wavelet auto-encoder, which can enhance the feature learning ability from the raw vibration signal of gearbox. Secondly, SWAE is constructed with a series of pre-trained wavelet auto-encoders for learning the deep features. Finally, the learned deep features are fed into the softmax classifier for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental gearbox signals, and the results show that the proposed method is more effective and robust than the traditional methods.
AB - Unsupervised feature learning and automatic fault diagnosis of gearbox is still a major challenge. For this purpose, a novel method called stacked wavelet auto-encoder (SWAE) is proposed in this paper. Firstly, wavelet function is adopted as the nonlinear activation function to design wavelet auto-encoder, which can enhance the feature learning ability from the raw vibration signal of gearbox. Secondly, SWAE is constructed with a series of pre-trained wavelet auto-encoders for learning the deep features. Finally, the learned deep features are fed into the softmax classifier for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental gearbox signals, and the results show that the proposed method is more effective and robust than the traditional methods.
KW - fault diagnosis
KW - gearbox
KW - stacked wavelet auto-encoder
KW - unsupervised feature learning
KW - wavelet function
UR - http://www.scopus.com/inward/record.url?scp=85062837843&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2018.8448870
DO - 10.1109/ICPHM.2018.8448870
M3 - 会议稿件
AN - SCOPUS:85062837843
T3 - 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
BT - 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
Y2 - 11 June 2018 through 13 June 2018
ER -