Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges, including the trade-off between global statistical feature efficiency and local transient signal fidelity, insufficient traceability of measurement features to underlying fault physics, and ineffective multi-source measurement information fusion across diagnostic scales. This paper presents a progressive physics-guided multi-scale vibration signal processing framework that addresses all three challenges within a unified diagnostic pipeline. An 81-dimensional measurement descriptor, derived from bearing kinematic theory and characteristic defect frequencies, establishes a physically traceable feature space enabling real-time fault screening at approximately 20 ms per sample. A fault-adaptive signal segmentation mechanism then directs analytical attention toward fault-relevant waveform regions guided by physics-based priors, without manual feature engineering. Structured fault mechanism knowledge is further encoded implicitly in model parameters during training, enabling autonomous multi-scale measurement fusion without external knowledge dependencies at inference. Validated on four public benchmark datasets under diverse operating conditions, the framework achieves 98.49% diagnostic accuracy with a 12.6-fold reduction in computational cost relative to signal-level baselines. Interpretability analysis confirms that diagnostic feature activations align with established bearing fault mechanics, supporting measurement traceability in safety-critical industrial systems.

翻译：基于振动的轴承故障诊断需要解决三个相互关联的测量挑战，包括全局统计特征效率与局部瞬态信号保真度之间的权衡、测量特征对底层故障物理机理的可追溯性不足，以及跨诊断尺度的多源测量信息融合效率低下的问题。本文提出一种渐进式物理引导的多尺度振动信号处理框架，在统一诊断流程中同时应对上述三项挑战。基于轴承运动学理论与特征缺陷频率构建的81维测量描述符，建立了物理可追溯的特征空间，可实现每样本约20毫秒的实时故障筛选。随后，故障自适应信号分割机制在物理先验引导下，无需人工特征工程即可将分析注意力聚焦于与故障相关的波形区域。训练过程中，结构化故障机理知识进一步隐式编码于模型参数中，实现在推理时无需外部知识依赖的自主多尺度测量融合。在四种公开基准数据集及多种运行工况下的验证表明，该框架实现了98.49%的诊断准确率，相比信号级基准方法计算成本降低12.6倍。可解释性分析证实，诊断特征激活模式与已建立的轴承故障力学机理相一致，为安全关键工业系统中的测量可追溯性提供了支持。