A deep reinforcement transfer convolutional neural network for rolling bearing fault diagnosis

Deep neural networks highly depend on substantial labeled samples when identifying bearing fault. However, in some practical situations, it is very difficult to collect sufficient labeled samples, which limits the application of deep neural networks in practical engineering. Therefore, how to use limited labeled samples to complete fault diagnosis tasks is an urgent problem. In this paper, a deep reinforcement transfer convolutional neural network (DRTCNN) is developed to tackle the problem. Firstly, an intelligent diagnosis agent constructed by a convolutional neural network is trained to obtain maximum long-term cumulative rewards, which is characterized by the ability to autonomously learn the latent relationship between fault samples and corresponding labels. Secondly, the parameter transfer learning method is utilized to establish a target task agent of DRTCNN. Finally, limited labeled target domain fault samples and the training mechanism of deep Q-network are employed to train the target task agent for performing target diagnosis tasks. Two diagnosis cases are conducted to verify the effectiveness of the proposed method when only limited labeled target domain fault samples are available.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a deep reinforcement transfer convolutional neural network (DRTCNN) for completing fault diagnosis tasks with limited labeled samples. The intelligent diagnosis agent is trained to learn the latent relationship between fault samples and labels, and the parameter transfer learning method is used to establish the target task agent. The target task agent is trained with limited labeled target domain fault samples and the training mechanism of deep Q-network to perform target diagnosis tasks.