Multiclass Fault Diagnosis and Novelty Detection of Induction Motor Using Deep Learning Algorithm Based on Frequency Domain Signal
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4721-8_8
Reference11 articles.
1. Karmakar S, Chattopadhyay S, Mitra M, Sengupta S. Induction motor and faults. Springer, Singapore, 2016.
2. Tran VT, Althobiani F, Ball A, Choi B. Expert systems with applications an application to transient current signal based induction motor fault diagnosis of Fourier—Bessel expansion and simplified fuzzy ARTMAP. Expert Syst Appl. 2013;40(13):5372–84.
3. Glowacz A. Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED. Arch Electr Eng. 2016;65:733–44.
4. Schoen RR, Lin BK, Habetler TG, Schlag JH, Farag S. An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans Ind Appl. 1995;31(6):1280–6.
5. Gangsar P, Tiwari R. Online diagnostics of mechanical and electrical faults in induction motor using multiclass support vector machine algorithms based on frequency domain vibration and current signals. ASCE-ASME J Risk nd Uncertainty in Eng Syst Part B Mech Eng. 2019;5(3): 031001.
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