2025/05/07 by Yuhan Yuan, Xiaomo Jiang, Yuan, Yuhan +12
Computer Science · Engineering · #Blind Source Separation Techniques #Discriminative model #Embedding #Encoder #Fault (geology) #Machine Fault Diagnosis Techniques #Magnetic Bearings and Levitation Dynamics #Noise (video) #Pattern recognition (psychology) #Robustness (evolution) #Transformer #cs.CV #eess.SP
paper · pdf · doi:10.48550/arxiv.2505.06285
openalex publication_date 2025/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Interpretable fault diagnosis (FD) plays a critical role in industrial manufacturing, as it improves human-machine understanding and operational efficiency. However, harsh operating environments often introduce strong background interference or noise, which weakens the discriminative capability and interpretability of existing FD methods. To address this issue, this paper proposes FE-MCFormer, a time-frequency fusion framework for robust and time-frequency interpretable fault diagnosis under strong noise conditions. A frequency adaptive learning layer (FALL) is developed to perform learnable spectral reconstruction, which explicitly suppresses noise-dominated frequency responses while preserving fault-sensitive harmonic structures. Furthermore, a multiscale time-frequency fusion (MSTFF) architecture is designed to jointly capture localized impulsive characteristics and structured global spectral interactions. Extensive experiments on a rolling bearing dataset and a real-world centrifugal compressor dataset demonstrate that the proposed method achieves stable and interpretable diagnostic performance under severe noise environments down to -10 dB SNR. The results indicate that FE-MCFormer provides an effective framework for turbomachinery fault diagnosis in complex noisy environments.