提出新型时频可解释模型，强噪声下仍能精准诊断机械故障
FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments
- 设计频率自适应学习层，动态抑制噪声频段保留故障特征
- 多尺度时频融合架构在-10 dB信噪比下仍保持稳定诊断性能
- 适合工业设备在复杂噪声环境中的故障检测与可解释分析
可解释的故障诊断（FD）在工业制造中至关重要，能提升人机协作效率。然而恶劣工况常带来强背景干扰或噪声，削弱现有方法的判别能力与可解释性。为此，本文提出FE-MCFormer，一种面向强噪声环境下鲁棒且时频可解释的故障诊断时频融合框架。设计频率自适应学习层（FALL），实现可学习的谱重建，显式抑制噪声主导的频响应，同时保留故障敏感的谐波结构。进一步构建多尺度时频融合（MSTFF）架构，联合捕捉局部冲击特性与全局频谱交互。在滚动轴承数据集和真实离心压缩机数据集上的大量实验表明，该方法在低至-10 dB信噪比的严苛噪声环境下仍具备稳定且可解释的诊断性能。结果表明，FE-MCFormer为复杂噪声环境中涡轮机械故障诊断提供了有效框架。
原文摘要 · Abstract (English)
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.
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