Identification of Unbalance in a Rotating System Using Artificial Neural Networks
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49791-9_22
Reference35 articles.
1. Jalali, M.H., Ghayour, M., Ziaei-Rad., S., Shahriari, B.: “Dynamic analysis of a high-speed rotor-bearing system”. Journal of the International Measurement Confederation 53, 1–9 (2014).
2. Chen, J., Lin, C., Peng, D., Ge, H.: “Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis”. IEEE Access 8, 224985–225003 (2020).
3. Tang, J., Guo, G., Wang, J., Xu, W.: “Fault injection and diagnosis of the centrifugal fan”. Journal of Physics: Conference Series 2366(1), (2022).
4. Zhang, X., Zhang, Z., Xu, J., Si, P., Liu, S.: “A new multi-disk shaft balancing method for rotating machinery using strain gauges”. Journal of Physics: Conference Series 2383(1), (2022).
5. Rahman, M.M., Uddin, M.N.: “Online Unbalanced Rotor Fault Detection of an IM Drive Based on Both Time and Frequency Domain Analyses”. IEEE Transactions on Industry Applications 53(4), 4087–4096 (2017).
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