TY - GEN
T1 - Rolling Bearing Fault Diagnosis Method Based on Stacked Denoising Autoencoder and Convolutional Neural Network
AU - Wang, Yumin
AU - Han, Minghong
AU - Liu, Wei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - The signal of rotating machine faults often exhibits strong nonlinearity and noise interference. Therefore. A fault diagnosis method towards non-stationary signal is proposed in this paper. A fault diagnosis model of combining stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed to solve the problem of difficult classification under strong noise environment. First, the SDAE model is utilized to reduce noise interference from the original data set. Then the processed data set is input into the CNN model for fault classification. The validity of the fault diagnosis model has been verified by the case western reserve university (CWRU) bearing data. The effectiveness of the method has been verified by comparison with other models.
AB - The signal of rotating machine faults often exhibits strong nonlinearity and noise interference. Therefore. A fault diagnosis method towards non-stationary signal is proposed in this paper. A fault diagnosis model of combining stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed to solve the problem of difficult classification under strong noise environment. First, the SDAE model is utilized to reduce noise interference from the original data set. Then the processed data set is input into the CNN model for fault classification. The validity of the fault diagnosis model has been verified by the case western reserve university (CWRU) bearing data. The effectiveness of the method has been verified by comparison with other models.
KW - convolutional neural network
KW - rolling bearing fault diagnosis
KW - stacked denoising autoencoder
UR - https://www.scopus.com/pages/publications/85082395005
U2 - 10.1109/QR2MSE46217.2019.9021126
DO - 10.1109/QR2MSE46217.2019.9021126
M3 - 会议稿件
AN - SCOPUS:85082395005
T3 - Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
SP - 833
EP - 838
BT - Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2019
Y2 - 6 August 2019 through 9 August 2019
ER -