{"ID":2828005,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15344","arxiv_id":"2512.15344","title":"Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of 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.","short_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 a...","url_abs":"https://arxiv.org/abs/2512.15344","url_pdf":"https://arxiv.org/pdf/2512.15344v1","authors":"[\"Hiroyoshi Nagahama\",\"Katsufumi Inoue\",\"Masayoshi Todorokihara\",\"Michifumi Yoshioka\"]","published":"2025-12-17T11:41:42Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"eess.SP\"]","methods":"[\"Transformer\"]","has_code":false}