Many fault diagnosis methods of rotating machines are based on discriminative features extracted from signals collected from the key components such as bearings. However, under complex operating conditions, periodic impulsive characteristics in the signal related to weak fault information are often obscured by noise interference. Consequently, existing approaches struggle to learn interpretable fault-related features in such scenarios. This paper proposes a novel transformer framework (FE-MCFormer) to extract interpretable time-frequency features, with the aim of improving the fault detection accuracy and intrepretability of rotating machines under strong noise. First, a Fourier adaptive reconstruction embedding layer is introduced as a global information encoder in the model. Subsequently, a time-frequency fusion module is designed, further improve the model robustness and interpretability. The effectiveness of FE-MCFormer in machine fault diagnosis is validated through three case studies.

翻译：许多旋转机械的故障诊断方法依赖于从轴承等关键部件采集的信号中提取判别性特征。然而，在复杂工况下，信号中与微弱故障信息相关的周期性冲击特征常被噪声干扰所掩盖，导致现有方法难以在此类场景下学习到可解释的故障相关特征。本文提出一种新颖的Transformer框架（FE-MCFormer），旨在提取可解释的时频特征，以提高强噪声环境下旋转机械故障检测的准确性与可解释性。首先，模型引入傅里叶自适应重构嵌入层作为全局信息编码器；随后，设计时频融合模块，以进一步提升模型的鲁棒性与可解释性。通过三项案例研究，验证了FE-MCFormer在机械故障诊断中的有效性。