Signal Analysis for High-Speed Train Damper Based on CPO-ICEEMDAN Algorithm

High-speed trains use anti-yaw dampers to reduce snaking vibrations during operation, ensuring smooth and safe travel. If these dampers fail, they create complex and unstable vibration signals, making it difficult for traditional signal analysis methods to work effectively. This study introduces an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method, optimized by the Crested Porcupine Optimizer (CPO). This approach enhances the clarity and stability of signal decomposition by adjusting noise parameters. We conducted simulation experiments on both normal and faulty from high-speed train anti-yaw dampers. The results show that the CPO-ICEEMDAN method outperforms traditional techniques in mode separation, fault feature extraction, and noise resistance. Compared to Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) methods, the CPO-ICEEMDAN method maintains stable decomposition even when there is significant noise interference.