The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded with prior knowledge, which brings the spatial information of the sensor network into the diagnosis algorithm and re-weights each sensor according to the diagnosis results. Firstly, the time–domain correlation of vibration signals between bogie sensor networks is calculated as the prior knowledge. Then, based on the spatial topological relationship of the sensors, the graph correlation matrix of the network is established. Further, the importance of each sensor is dynamically analyzed and updated together with the training process. The proposed method is tested on a high-precision bogie test bed, and the experimental results demonstrate the effectiveness and superiority of the proposed method.

Emerging deep learning-based fault diagnosis methods have advanced in the current industrial scenarios of various working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address the challenge of fault diagnosis under unseen working conditions, a domain generation framework for unseen conditions fault diagnosis is proposed, which consists of an Adaptive Feature Fusion Domain Generation Network (AFFN) and a Mix-up Augmentation Method (MAM) for both the data and domain spaces. AFFN is utilized to fuse domain-invariant and domain-specific representations to improve the model’s generalization performance. MAM enhances the model’s exploration ability for unseen domain boundaries. The diagnostic framework with AFFN and MAM can effectively learn more discriminative features from multiple source domains to perform different generalization tasks for unseen working loads and machines. The feasibility of the proposed unseen conditions diagnostic framework is validated on the SDUST and PU datasets and achieved peak diagnostic accuracies of 94.15% and 93.27%, respectively.