Fault diagnosis of rolling bearing using a transfer ensemble deep reinforcement learning method

Abstract

The reliable operation of rolling bearings is related to machinery safety. However, fault signals encountered in practical engineering applications are often characterized by high-dimensionality, complexity, and volume, which restricts the application of deep neural networks in fault diagnosis. Additionally, conventional diagnostic methods are limited by their reliance on manual feature extraction and a significant quantity of labeled samples, which can be time-consuming and resource-intensive. To address these limitations and improve the performance of fault diagnosis in the absence of labeled samples, an intelligent diagnostic agent (TERL-Agent) that combines transfer learning, ensemble learning and reinforcement learning is proposed. Firstly, an intelligent diagnostic agent is constructed by ensemble learning, which combines multiple reinforcement learning agents based on the Deep Q Network structure and has interactive learning capability to learn and classify fault data in the source domain environment. Secondly, transfer learning is used to transfer the feature extraction ability of the source domain intelligent diagnostic agent to the target intelligent diagnostic agent. Finally, the obtained target intelligent diagnostic agent is evaluated on fault data in the target domain and compared with other methods. The results indicate that the proposed method exhibits remarkable advantages and has great potential for practical application in fault diagnosis.