A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors-Reference-Cited by-同舟云学术

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

Published:2023 Issue:5 Volume:20 Page:9268-9287
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Zhang Tangsheng¹,Zhi Hongying²

Affiliation:

1. School of Intelligent Manufacturing Engineering, Shanxi Institute of Science and Technology, Jincheng 030021, Shanxi, China

2. School of Applied Science, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China

Abstract

<abstract> <p>Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

Reference45 articles.

1. Z. Guo, K. Yu, N. Kumar, W. Wei, S. Mumtaz, M. Guizani, Deep distributed learning-based POI recommendation under mobile edge networks, IEEE Internet Things J., 10 (2022), 303–317. https://doi.org/10.1109/JIOT.2022.3202628

2. Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mob. Comput., 21 (2020), 2130–2142. https://doi.org/10.1109/TMC.2020.3033563

3. Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi, M. Guizani, Deep information fusion-driven POI scheduling for mobile social networks, IEEE Network, 36 (2022), 210–216. https://doi.org/10.1109/MNET.102.2100394

4. L. Yang, Y. Li, S. X. Yang, Y. Lu, T. Guo, K. Yu, Generative adversarial learning for intelligent trust management in 6G wireless networks, IEEE Network, 36 (2022), 134–140. https://doi.org/10.1109/MNET.003.2100672

5. Q. Zhang, K. Yu, Z. Guo, S. Garg, J. J. P. C. Rodrigues, M. M. Hassan, et al., Graph neural networks-driven traffic forecasting for connected internet of vehicles, IEEE Trans. Network Sci. Eng., 9 (2022), 3015–3027. https://doi.org/10.1109/TNSE.2021.3126830