The relationship between shaft vibration and bearing vibration under imbalanced state based on homologous information fusion under imbalanced state
Shaft vibration and bearing vibration are important parameters reflecting the dynamic behavior of the rotor-bearing-supporting system, and they have a significant impact on the operating state and safety of equipment. However, obtaining relevant signals of shaft vibration or bearing vibration often faces many challenges in actual working conditions, mainly due to the limitations of measuring equipment, environmental interference, and the complexity of operating conditions. Therefore, understanding the correlation between shaft vibration and bearing vibration can not only realize signal complementarity and improve the comprehensiveness and accuracy of data, but also provide a more accurate basis for fault diagnosis and condition monitoring. Therefore, this study constructs an unbalanced fault dynamics model based on the short-bearing theory. The shaft vibration and bearing vibration signals predicted by the model are obtained through the numerical integration technique. Secondly, the full-vector spectrum technology based on homologous information fusion is adopted to conduct a two-channel fusion analysis of these signals. Finally, a rotor experimental platform is constructed and corresponding experimental verifications are carried out to verify the accuracy of these analysis results. The experimental results confirm that obtaining this complementary relationship enables us to infer the operation health state of equipment through the changing trend of some parameters even in the absence of a certain measurement signal, and then formulate corresponding maintenance and management strategies, thereby improving the reliability and operating efficiency of equipment.
Authors
- Shijin Chen (ORCID: https://orcid.org/0000-0002-3017-5595)
- Hongchao Wang (ORCID: https://orcid.org/0000-0002-4061-802X)
Publication Details
- Journal
- Journal of Vibroengineering
- Published
- 2025-03-11
- DOI
- https://doi.org/10.21595/jve.2025.24640
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00