Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

Author:

Singh Vikas,Gangsar PurushottamORCID,Porwal Rajkumar,Atulkar A.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference166 articles.

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2. Akar, M., & Cankaya, I. (2012). Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic. Turkish Journal of Electrical Engineering & Computer Sciences, 20(1), 1077–1089.

3. Akbari, M., Homaei, H., & Heidari, M. (2014). An intelligent fault diagnosis approach for gears and bearings based on wavelet transform as a preprocessor and artificial neural networks. International Journal of Mathematical Modeling & Computations, 4(04), 309–329.

4. Al Tobi, M. A. S., Bevan, G., Ramachandran, K. P., Wallace, P., & Harrison, D. (2017). Experimental set-up for investigation of fault diagnosis of a centrifugal pump. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 11(3), 481–485.

5. Al-Deen, K. N., Hummes, D., Fruth, B., Caironi, C., Ghaffar, A. A. and Karas, M. (2018). Signature analysis as a medium for faults detection in induction motors. In 2018 International Conference on Computing Sciences and Engineering (ICCSE) (pp. 1-6). IEEE.

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