Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918252261670912 |
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| author | Nagahama, Hiroyoshi Inoue, Katsufumi Todorokihara, Masayoshi Yoshioka, Michifumi |
| author_facet | Nagahama, Hiroyoshi Inoue, Katsufumi Todorokihara, Masayoshi Yoshioka, Michifumi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery Nagahama, Hiroyoshi Inoue, Katsufumi Todorokihara, Masayoshi Yoshioka, Michifumi Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2512.15344 |