通过相位对齐提升旋转机械故障诊断准确率
Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery
- 提出两种相位对齐方法：独立对齐各轴与统一参考对齐
- 统一参考法最高达96.2%准确率，提升5.4个百分点
- 适用于多轴振动信号的故障诊断系统优化
旋转机械的预测性维护越来越依赖振动信号，但大多数基于学习的方法在频谱特征提取中丢弃相位信息，或仅使用原始时域波形而未显式利用相位。本文针对多轴振动数据中的随机相位变化，提出两种相位感知预处理策略：(1) 三轴独立相位调整，将每轴单独对齐至零相位；(2) 单轴参考相位调整，通过施加统一时间偏移保留轴间相位关系。基于同步三轴传感器采集的新构建转子数据集，在两阶段学习框架下评估六种深度学习架构。结果表明，两种方法均带来架构无关的性能提升：三轴独立法在Transformer上获得+2.7%增益；单轴参考法表现更优，最高达96.2%准确率（+5.4%）。研究证实这两种相位对齐策略是预测性维护系统的实用且可扩展的增强手段。
原文摘要 · Abstract (English)
Predictive maintenance of rotating machinery increasingly relies on vibration signals, yet most learning-based approaches either discard phase during spectral feature extraction or use raw time-waveforms without explicitly leveraging phase information. This paper introduces two phase-aware preprocessing strategies to address random phase variations in multi-axis vibration data: (1) three-axis independent phase adjustment that aligns each axis individually to zero phase (2) single-axis reference phase adjustment that preserves inter-axis relationships by applying uniform time shifts. Using a newly constructed rotor dataset acquired with a synchronized three-axis sensor, we evaluate six deep learning architectures under a two-stage learning framework. Results demonstrate architecture-independent improvements: the three-axis independent method achieves consistent gains (+2.7\% for Transformer), while the single-axis reference approach delivers superior performance with up to 96.2\% accuracy (+5.4\%) by preserving spatial phase relationships. These findings establish both phase alignment strategies as practical and scalable enhancements for predictive maintenance systems.
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