Condition Monitoring of Broken Rotor Bars Using a Hybrid FMM-GA Model

被引：0
|
作者：
Seera, Manjeevan [1 ]
Lim, Chee Peng [2 ]
Loo, Chu Kiong [1 ]
机构：
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia
[2] Deakin Univ, Ctr Intelligent Syst Res, Geelong, Vic, Australia
来源：
NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III | 2014年 / 8836卷
关键词：
Condition monitoring; fault diagnosis; fuzzy min-max neural network; genetic algorithms; induction motor; INDUCTION-MOTORS; NEURAL-NETWORKS; CLASSIFICATION; MAINTENANCE; FAULTS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don't care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.
引用
收藏
页码：381 / 389
页数：9
相关论文
共 50 条
  • [31] Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis
    Wang, Zuolu
    Yang, Jie
    Li, Haiyang
    Zhen, Dong
    Xu, Yuandong
    Gu, Fengshou
    ENERGIES, 2019, 12 (17)
  • [32] Fault diagnosis of broken rotor bars in induction motor using multiscale entropy and backpropagation neural network
    Verma, Alok
    Sarangi, Somnath
    Advances in Intelligent Systems and Computing, 2015, 343 : 393 - 404
  • [33] Mono-Dimensional Demodulation Techniques for Diagnosing Broken Rotor Bars Faults Within Inverter-Fed Induction Machine at Low Load Operation Condition
    Khelfi, H.
    Hamdani, S.
    2021 IEEE 13TH INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRICAL MACHINES, POWER ELECTRONICS AND DRIVES (SDEMPED), 2021, : 512 - 517
  • [34] Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation
    Saïd, MSN
    Benbouzid, MEH
    Benchaib, A
    IEEE TRANSACTIONS ON ENERGY CONVERSION, 2000, 15 (01) : 66 - 70
  • [35] INFLUENCE OF BROKEN ROTOR BARS LOCATION IN THE SQUIRREL CAGE INDUCTION MOTOR USING FINITE ELEMENT METHOD
    Halem, N.
    Zouzou, S. E.
    Srairi, K.
    Guedidi, S.
    JOURNAL OF FUNDAMENTAL AND APPLIED SCIENCES, 2013, 5 (01) : 110 - 119
  • [36] A NARX Model-Based Condition Monitoring Method for Rotor Systems
    Gao, Yi
    Yu, Changshuai
    Zhu, Yun-Peng
    Luo, Zhong
    SENSORS, 2023, 23 (15)
  • [37] A STOCHASTIC MODEL FOR INDIRECT CONDITION MONITORING USING PROPORTIONAL COVARIATE MODEL
    Rabbani, M.
    Manavizadeh, N.
    Balali, S.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2008, 21 (01): : 45 - 56
  • [38] Exact Modeling and Simulation of Saturated Induction Motors with Broken Rotor Bars Fault using Winding Function Approach
    Ojaghi, M.
    Sabouri, M.
    Faiz, J.
    Ghorbanian, V.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2014, 27 (01): : 69 - 78
  • [39] Rotor resistance estimation of an induction motor to detect broken bars fault using H-H method
    Nait-Said, N
    Nait-Said, MS
    Bouguechal, N
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2004, 32 (02) : 149 - 161
  • [40] Broken Rotor Bars Fault Detection in Induction Motors Using Park's Vector Modulus and FWNN Approach
    Guo, Qianjin
    Li, Xiaoli
    Yu, Haibin
    Hu, Wei
    Hu, Jingtao
    ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT 2, PROCEEDINGS, 2008, 5264 : 809 - +
12345