TY - JOUR
T1 - A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis
AU - Li, Lintao
AU - Jiang, Hongkai
AU - Wang, Ruixin
AU - Yang, Qiao
N1 - Publisher Copyright:
© 2023 IOP Publishing Ltd
PY - 2023/11
Y1 - 2023/11
N2 - The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.
AB - The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.
KW - fault diagnosis
KW - neural architecture search
KW - optimization algorithm
KW - reinforcement learning
KW - rolling bearing
UR - http://www.scopus.com/inward/record.url?scp=85167868685&partnerID=8YFLogxK
U2 - 10.1088/1361-6501/acec06
DO - 10.1088/1361-6501/acec06
M3 - 文章
AN - SCOPUS:85167868685
SN - 0957-0233
VL - 34
JO - Measurement Science and Technology
JF - Measurement Science and Technology
IS - 11
M1 - 115122
ER -