Digital Twin and Deep Learning-Based Approach for Detecting Faults in Induction Motors
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70018-7_44
Reference16 articles.
1. Garcia-Guevara, F.M., Villalobos-Piña, F.J., Alvarez-Salas, R., Cabal-Yepez, E., Gonzalez-Garcia, M.A.: Stator fault detection in induction motors by autoregressive modeling. Mathem. Problems Eng. 2016 (2016).
2. Saari, J., Strömbergsson, D., Lundberg, J., Thomson, A.: Detection and identification of windmill bearing faults using a one-class support vector machine (SVM). Measurement 137, 287–301 (2019)
3. Drakaki, M., Karnavas, Y.L., Tziafettas, I.A., Linardos, V., Tzionas, P.: Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey. J. Indust. Eng. Manag. (JIEM) 15(1), 31–57 (2022)
4. Mukhopadhyay, R., Panigrahy, P. S., Misra, G., Chattopadhyay, P.: Quasi 1D CNN-based fault diagnosis of induction motor drives. In: 2018 5th International Conference on Electric Power and Energy Conversion Systems (EPECS), pp. 1–5. IEEE (April 2018)
5. Khan, T., Alekhya, P., Seshadrinath, J.: Incipient inter-turn fault diagnosis in induction motors using CNN and LSTM based Methods. In: 2018 IEEE Industry Applications Society Annual Meeting (IAS), pp. 1–6. IEEE (September2018 )
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