Fault Prediction in Induction Motor Using Artificial Neural Network Algorithms
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8986-7_27
Reference10 articles.
1. Tiwari R. Rotor systems: analysis and identification. Boca Raton: CRC Press; 2017.
2. Samanta S, Bera JN, Sarkar G. KNN based fault diagnosis system for induction motor. In: 2nd international conference on control, instrumentation, energy & communication (CIEC). Jan 28–30, 2016. https://doi.org/10.1109/CIEC.2016.7513791.
3. Nguyen NT, Kwon JM, Lee HH. Fault diagnosis of induction motor using decision tree with an optimal feature selection. In: 7th international conference on power electronics. 2007. https://doi.org/10.1109/ICPE.2007.4692484.
4. Gangsar P, Tiwari R. Taxonomy of induction-motor mechanical-fault based on time-domain vibration signals by multiclass SVM classifiers. Intell Ind Syst. 2016. https://doi.org/10.1007/s40903-016-0053-x.
5. Nguyen NT, Lee HH. An application of support vector machines for induction motor fault diagnosis with using genetic algorithm. In: International conference on intelligent computing. Springer; 2008. https://doi.org/10.1007/978-3-540-85984-0_24.
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