TY - JOUR
T1 - Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network
AU - Jiang, Hongkai
AU - Li, Xingqiu
AU - Shao, Haidong
AU - Zhao, Ke
N1 - Publisher Copyright:
© 2018 IOP Publishing Ltd.
PY - 2018/5/10
Y1 - 2018/5/10
N2 - Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.
AB - Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.
KW - adaptive learning rate
KW - deep learning
KW - improved deep recurrent neural network
KW - intelligent fault diagnosis
KW - rolling bearing
UR - http://www.scopus.com/inward/record.url?scp=85047267266&partnerID=8YFLogxK
U2 - 10.1088/1361-6501/aab945
DO - 10.1088/1361-6501/aab945
M3 - 文章
AN - SCOPUS:85047267266
SN - 0957-0233
VL - 29
JO - Measurement Science and Technology
JF - Measurement Science and Technology
IS - 6
M1 - 065107
ER -