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

2025-12-17

AI Summary

In rotating machinery fault diagnosis, phase information has long been neglected or discarded, with its practical impact lacking systematic empirical validation. This paper systematically reveals the critical role of phase alignment in multi-axis vibration-based diagnosis. We propose two phase-aware preprocessing strategies: (1) axis-wise independent phase zeroing and (2) single-axis reference-based phase alignment—which preserves inter-axis spatial phase relationships and significantly enhances robustness. Leveraging a custom-synchronized tri-axial rotor vibration dataset, we integrate time-domain phase calibration with spectrum-domain phase-sensitive modeling within a two-stage deep learning framework across six architectures. Experimental results show that the single-axis reference alignment achieves 96.2% classification accuracy—outperforming the baseline by 5.4%. Axis-wise independent alignment also yields consistent gains across diverse models (e.g., +2.7% for Transformer), demonstrating the universal utility of phase information in vibration-based fault diagnosis.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Addresses random phase variations in multi-axis vibration data
• Introduces phase-aware preprocessing strategies for fault diagnosis
• Evaluates deep learning architectures with phase alignment methods

Innovation

• Phase-aware preprocessing strategies for vibration signals
• Independent and reference phase adjustment methods
• Deep learning evaluation with synchronized three-axis sensor data