Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery

TL;DR

This study explores how incorporating phase information in vibration signal preprocessing improves fault diagnosis accuracy in rotating machinery, demonstrating two effective phase alignment strategies across multiple deep learning models.

Contribution

It introduces two novel phase-aware preprocessing methods for vibration data and evaluates their effectiveness across various deep learning architectures.

Findings

Single-axis reference phase adjustment achieves up to 96.2% accuracy.

Three-axis independent phase adjustment improves Transformer performance by 2.7%.

Phase-aware preprocessing enhances fault diagnosis in rotating machinery.

Abstract

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.