Siamese ordinal prototypical network with S-transform for few-shot damage severity assessment of high-speed train bearings

Abstract

Axle box bearings are critical rotating components in the running gear of high-speed trains, and their damage condition directly affects the operational safety of trains. To address the problems of non-stationary vibration signals, limited fault samples, and the ordinal degradation characteristics of bearing damage levels, this paper proposes a bearing damage severity assessment method based on S-transform (ST) and a Siamese ordinal prototypical network (ProtoNet). First, the ST is used to convert one-dimensional vibration signals into two-dimensional time–frequency images to enhance fault feature representation. Then, a Swin Transformer is employed to extract features from the time–frequency images, and a Siamese network is constructed to optimize the feature space distribution through contrastive loss. On this basis, an ordinal ProtoNet combined with the Wasserstein distance loss is introduced to impose ordinal constraints on different damage levels, enabling effective identification of bearing damage severity. Experiments are conducted on a high-speed train bearing vibration dataset under a few-shot setting, and comparisons are made with convolutional neural network (CNN), ProtoNet, Siamese CNN, ResNet18, Swin Transformer, and convolutional block attention module-residual network-Siamese models. The results show that the proposed method achieves a classification accuracy of 93.8% in a four-class bearing condition recognition task, outperforming the compared models and demonstrating its effectiveness in small-sample bearing damage severity assessment.