Prognostics and health management for induction machines: a comprehensive review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02103-6.pdf
Reference146 articles.
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3. Atta, M.E.E.-D., Ibrahim, D. K., & Gilany, M. I. (2022). Broken bar fault detection and diagnosis techniques for induction motors and drives: State of the art. IEEE Access, 10, 88504–88526. https://doi.org/10.1109/ACCESS.2022.3200058
4. Barbieri, M., Nguyen, K. T. P., Diversi, R., Medjaher, K., & Tilli, A. (2021). RUL prediction for automatic machines: A mixed edge-cloud solution based on model-of-signals and particle filtering techniques. Journal of Intelligent Manufacturing, 32(5), 1421–1440. https://doi.org/10.1007/s10845-020-01696-6
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