With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained by plenty of data. In this work, we proposed a reinforcement learning system for fault detection and diagnosis. No assumption is required. Feature exaction is first made. Then with the features as the states of the environment, the agent directly interacts with the environment. Optimal policy, which determines the exact category, size and location of the fault, is obtained by updating Q values. The method takes advantage of expert knowledge. When the features are unclear, action will be made to get more information from the new state for further determination. We create recurrent neural network with the long short-term memory architecture to approximate Q values. The application on a motor is discussed. The experimental results validate that the proposed method demonstrates a significant improvement compared with existing state-of-the-art methods of fault detection and diagnosis.

Classification; reinforcement learning; neural network; feature exaction and selection; fault detection and diagnosis