Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Immunology,Immunology and Allergy
Reference35 articles.
1. Kumar RR, Cirrincione G, Cirrincione M, Tortella A, Andriollo M (2020) Induction machine fault detection and classification using non-parametric, statistical-frequency features and shallow neural networks. IEEE Trans Energy Convers 36:1070–1080
2. Gundewar Swapnil K, Kane Prasad V (2021) Condition monitoring and fault diagnosis of induction motor. J Vib Eng Technol 9:643–674
3. Kim BS, Lee SH, Lee MG, Ni J, Song JY, Lee CW (2007) A comparative study on damage detection in speed-up and coast-down process of grinding spindle-typed rotor-bearing system. J Mater Process Technol 187:30–36
4. Yazidi A, Hena H, Capolino GA, Artioli M, Filippetti F (2005) Improvement of frequency resolution for three-phase induction machine fault diagnosis. In: Fourtieth IAS Annual Meeting. Conference Record of the 2005 IEEE Industry Applications Conference 1:20–25
5. Kia SH, Cirrincione G, Henao H, Capolino GA (2016) A computationally efficient algorithm devoted to gear tooth localized fault detection in induction machine-based systems. In: 2016 XXII IEEE International Conference on Electrical Machines (ICEM) 2144–2150
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Demodulation-Based Spectral Analysis of Input Current with Gabor Transform in Detection of Electrical Faults in Induction Motors;2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE);2023-06-15
2. Motor Bearing Fault Prediction Using Artificial Intelligence Techniques;2023 International Conference on Microwave, Optical, and Communication Engineering (ICMOCE);2023-05-26