TY - JOUR
T1 - An adaptive deep convolutional neural network for rolling bearing fault diagnosis
AU - Fuan, Wang
AU - Hongkai, Jiang
AU - Haidong, Shao
AU - Wenjing, Duan
AU - Shuaipeng, Wu
N1 - Publisher Copyright:
© 2017 IOP Publishing Ltd.
PY - 2017/8/16
Y1 - 2017/8/16
N2 - The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods.
AB - The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods.
KW - adaptive deep convolutional neural network
KW - fault diagnosis
KW - feature learning
KW - particle swarm optimization
KW - rolling bearing
UR - http://www.scopus.com/inward/record.url?scp=85028438687&partnerID=8YFLogxK
U2 - 10.1088/1361-6501/aa6e22
DO - 10.1088/1361-6501/aa6e22
M3 - 文章
AN - SCOPUS:85028438687
SN - 0957-0233
VL - 28
JO - Measurement Science and Technology
JF - Measurement Science and Technology
IS - 9
M1 - 095005
ER -