Research on Switch Machine Fault Diagnosis Based on VMD-1DCNN-BiLSTM

To enhance the intelligence level of switch machine fault diagnosis, this paper proposes an end-to-end diagnostic model integrating Variational Mode Decomposition (VMD), 1D Convolutional Neural Network (1DCNN), and Bidirectional Long Short-Term Memory (BiLSTM). First, VMD is employed to adaptively decompose raw traction force signals into multiple Intrinsic Mode Functions (IMFs) with specific frequency-band characteristics. This multi-scale decomposition enhances the representation of critical fault-related features while effectively suppressing noise interference. Subsequently, 1DCNN extracts localized time-frequency features from each modal component. Finally, a BiLSTM network is introduced to comprehensively capture bidirectional dependencies within temporal features, thereby strengthening the model's ability to perceive fault pattern evolution and enabling precise classification of switch machine faults. Experimental results demonstrate that the proposed VMD-1DCNN-BiLSTM model outperforms conventional 1DCNN, BiLSTM, and CNN-LSTM models in metrics including precision, recall, and F1-score, achieving an overall diagnostic accuracy exceeding 97 %. The model exhibits superior feature extraction and temporal modeling capabilities. Compared to existing methods predominantly reliant on electrical signals such as current or power, this study adopts traction force curves - a direct indicator of switch machine operational states - as the core diagnostic signal. This approach enables earlier detection and enhanced sensitivity to latent fault precursors, providing a more efficient and reliable solution for condition assessment of switch machines and intelligent maintenance of turnout systems.