Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning

Abstract

Early fault diagnosis of suspension systems is essential for the safe operation of high-speed trains. However, neural network-based fault diagnosis methods have two remaining issues: the fault samples are difficult to obtain in practice and the early fault features are too weak to be extracted directly from the raw vibration signals using neural networks. A novel strategy is proposed for early faults diagnosis in suspension systems (i.e. component performance degradation within 20%) by integrating a new sample reconstruction method, a new grouping normalisation method, and model-agnostic meta-learning (MAML) algorithm. First, the 1D raw vibration signals are converted to 2D feature matrices consisting of artificial features using the sample reconstruction method; meanwhile, the grouping normalisation method is used to enhance the early weak fault features in the feature matrices. Second, MAML specialises in few-shot model training for early fault diagnosis, with the feature matrices as the training samples. Finally, the results are compared with those obtained using other current methods. The numerical results show that the proposed strategy yielded excellent performance in the few-shot early faults diagnosis of suspension systems, achieving a maximum accuracy of 94.75%.