Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

Abstract

Deep-learning-based methods have been successfully applied to fault diagnosis of rotating machinery. However, the domain mismatch among different operating conditions significantly deteriorates diagnostic performance of these methods in industrial applications. To solve this problem, a new fault diagnosis model based on capsule neural network (Cap-net) is constructed, and a novel online domain adaptation learning method based on deep reinforcement learning (DRL) is proposed in this article to improve the adaptivity of the fault diagnosis model. In this method, the Cap-net is first introduced into the DRL as an agent to extract representative features and diagnoses fault. Moreover, the online domain adaptation learning of the agent is conducted based on the Q-learning of the DRL so as to adapt to different operating domains that have never been experienced. Specifically, an online feature dictionary combined with cosine similarity is designed to coarsely label the online data collected from different operating domain, while a reward mechanism is defined to evaluate the obtained label. Subsequently, the online data, the corresponding label, and the reward are used to optimize the agent to obtain the desired diagnostic model. Two experiment studies are implemented to verify the effectiveness of the proposed method. The experimental results show that the proposed method has more excellent diagnostic performance and adaptivity than the existing popular methods.