A deep learning method for motor fault diagnosis based on a capsule network with gate-structure dilated convolutions

Author:

Liang Yu,Li Binbin,Jiao Bin

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference36 articles.

1. Ali MZ, Liang XD (2019) Induction motor fault diagnosis using discrete wavelet transform. In: 2019 IEEE canadian conference of electrical and computer engineering (CCECE), Edmonton, AB, Canada, pp. 1–4

2. Dick P, Carl H, Nader S, Alireza MA, Sarabjeet S (2015) Analysis of bearing stiffness variations, contact forces and vibrations in radially loaded double row rolling element bearings with raceway defects. Mech Syst Signal Process 50–51:139–160

3. Zhu XY, Wang YJ (2018) Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator. J Vib Shock 37(6):104–110

4. Jerkan DG, Reljić DD, Marčetić DP (2017) Broken rotor bar fault detection of im based on the counter-current braking method. IEEE Trans Energy Convers 32(4):1356–1366

5. Singh S, Kumar N (2017) Detection of bearing faults in mechanical systems using stator current monitoring. IEEE Trans Ind Inf 13(3):1341–1349

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