Research on intelligent fault diagnosis for railway point machines using deep reinforcement learning

The advanced diagnosis of faults in railway point machines is crucial for ensuring the smooth operation of the turnout conversion system and the safe functioning of trains. Signal processing and deep learning-based methods have been extensively explored in the realm of fault diagnosis. While these approaches effectively extract fault features and facilitate the creation of end-to-end diagnostic models, they often demand considerable expert experience and manual intervention in feature selection, structural construction and parameter optimization of neural networks. This reliance on manual efforts can result in weak generalization performance and a lack of intelligence in the model. To address these challenges, this study introduces an intelligent fault diagnosis method based on deep reinforcement learning (DRL). Initially, a one-dimensional convolutional neural network agent is established, leveraging the specific characteristics of point machine fault data to automatically extract diverse features across multiple scales. Subsequently, deep Q network is incorporated as the central component of the diagnostic framework. The fault classification interactive environment is meticulously designed, and the agent training network is optimized. Through extensive interaction between the agent and the environment using fault data, satisfactory cumulative rewards and effective fault classification strategies are achieved. Experimental results demonstrate the proposed method's high efficacy, with a training accuracy of 98.9% and a commendable test accuracy of 98.41%. Notably, the utilization of DRL in addressing the fault diagnosis challenge for railway point machines enhances the intelligence of diagnostic process, particularly through its excellent independent exploration capability.