A sparse Ramanujan refined mode decomposition method based on spectrum differential reconstruction
Kai Li, Jian Cheng, Haiyang Pan, Jinde Zheng, Xun Huang · Structural Health Monitoring · 2026
To address the difficulty in extracting fault features due to severe multi-source coupling and wide energy differences in composite faults, a sparse Ramanujan refined mode decomposition (SDR-SRRMD) method based on spectrum differential reconstruction is proposed. On the one hand, the SDR-SRRMD method distinguishes spectral lines based on the discrepancy between the original spectrum and the preprocessed spectrum derived via optimal weight impulse extraction. Based on this discrimination, the proposed method applies filtering to the original signal, which can achieve effective separation of the impulse components through reconstruction. On the other hand, the SDR-SRRMD method achieves accurate extraction of multi-period impulse components by constructing a sparse Ramanujan subspace and projecting the impulse components into their respective subspaces. Simulation and experimental signal analysis results demonstrate that this method can effectively separate and extract multi-period impulse components, serving as an effective solution for composite fault diagnosis.