Few-Shot Fault Diagnosis of Railway Switch Machines Using Regularized Supervised Contrastive Meta-Learning

Railway switch machines are key devices in railway signal systems and have a critical impact on train operation safety. However, in real operating conditions, fault samples are scarce because field data collection is cumbersome and often constrained by safety requirements, which limits the diagnostic accuracy and generalization capability of traditional fault diagnosis methods in few-shot scenarios. To address the challenge posed by insufficient accuracy in railway switch machine state recognition using sensors under few-shot conditions, we propose a regularized supervised contrastive meta-learning (RSCML) fault diagnosis method for switch machines. First, the tri-axial vibration signals acquired from the throwing rod and the reducer are transformed into axis-wise STFT spectrograms and organized as a unified three-channel time-frequency representation for subsequent cross-channel feature learning. Second, channel expansion and attention enhancement are employed to obtain more informative feature representations among similar fault types under limited samples. Finally, the feature extractor is integrated into the regularized supervised contrastive ANIL framework, while multi-loss optimization and stability regularization jointly constrain the meta-learning training process. Experimental results show that the proposed method achieves a maximum accuracy of 99.73% on 3-way and 5-way few-shot tasks, together with an F1-score of up to 99.72%. In the cross-category generalization experiment, it achieves a 93.08% accuracy and a 92.84% F1-score, indicating improved robustness when the fault categories at test time differ from those used during meta-training. The proposed method shows superior classification performance and stronger generalization to unseen fault categories under the current dataset setting, which suggests promising potential for switch machine fault diagnosis under limited sample conditions.