Fault Diagnosis Method for Boost Chopper of High-Speed Maglev Train Based on Deep Time-Series Modeling

The boost chopper (HS) is a core electrical component of the 440 V grid in high-speed maglev trains, providing reliable power for battery charging and auxiliary systems. Fault diagnosis of the HS is crucial for identifying operational faults and ensuring stable train operation. However, HS faults exhibit both long-period fluctuations and transient characteristics, which are difficult for a single network to capture synchronously. This paper proposes a multi-scale fault diagnosis method based on a TimesNet-CNN dual-branch architecture, constructing a parallel and complementary feature extraction mechanism. The TimesNet branch uses Fast Fourier Transform (FFT) to adaptively identify dominant periods, reshaping the 1D sequence into a 2D structure to explicitly model the global evolution of intra-period fluctuations and inter-period trends via Inception convolution. Meanwhile, the CNN branch employs stacked small convolutional kernels and hierarchical downsampling to extract local high-frequency anomaly features. After feature fusion, the method achieves synergistic discrimination of global periodicity and local transiency. Finally, experiments were conducted on a real-world dataset containing 11 system states (10 fault types and 1 normal state). Experimental results show that the proposed method outperforms TimesNet, CNN, ResNet and Informer models in precision, recall and F1-score. This validates the effectiveness of the dual-branch feature fusion mechanism in capturing multi-scale fault features, achieving high-precision identification of HS faults.