Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine

被引:72
|
作者
Pandarakone, Shrinathan Esakimuthu [1 ]
Mizuno, Yukio [1 ]
Nakamura, Hisahide [2 ]
机构
[1] Nagoya Inst Technol, Nagoya, Aichi 4668555, Japan
[2] TOENEC Corp, Nagoya, Aichi 4570819, Japan
关键词
Bearing damage; condition monitoring; fault diagnosis; induction motor; spectral analysis; stator current; support vector machine; ROLLING ELEMENT BEARINGS; DIAGNOSIS; CLASSIFICATION; VIBRATION; FEATURES; SIGNALS;
D O I
10.1109/TIA.2016.2639453
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In modern industrial environment, the demand for condition monitoring and maintenance management for the induction motor has increased. Among all the components of the induction motor, bearing is the critical component and the fault occurring in it has to be considered as a major issue. Usually, the bearing fault can be detected by the vibrational analysis. However, this method has a disadvantage that location of the equipment is not always easily accessible, and also it is quite costly. Thus, in this paper, an experiment for detecting the fault in the bearing of a three phase induction motor is achieved by the frequency selection in the stator-current spectrum. Their feature was evaluated by the fast Fourier transform and the diagnosis was performed by a support vector machine. Experimental results were obtained considering two types of outer raceway bearing faults at different load conditions and promising results were obtained.
引用
收藏
页码:3049 / 3056
页数:8
相关论文
共 50 条
  • [1] Induction Motor Fault Identification using Support Vector Machine
    Okpo, Ekom E.
    Le Roux, Peet F.
    Nnachi, Agha F.
    2023 6TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY AND POWER ENGINEERING, REPE 2023, 2023, : 168 - 174
  • [2] Condition Monitoring and Fault Diagnosis of Induction Motor Using Support Vector Machine
    Patel, Rakesh A.
    Bhalja, Bhavesh R.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2016, 44 (06) : 683 - 692
  • [3] Bearing Fault Detection of Induction Motor Using SWPT and DAG Support Vector Machines
    Ben Abid, Firas
    Zgarni, Slaheddine
    Braham, Ahmed
    PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2016, : 1476 - 1481
  • [4] Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
    Konar, P.
    Chattopadhyay, P.
    APPLIED SOFT COMPUTING, 2011, 11 (06) : 4203 - 4211
  • [5] Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods
    Kim, Min-Chan
    Lee, Jong-Hyun
    Wang, Dong-Hun
    Lee, In-Soo
    SENSORS, 2023, 23 (05)
  • [6] Rolling Bearing Fault Classification Based on Envelope Spectrum and Support Vector Machine
    Guo, Lei
    Chen, Jin
    Li, Xinglin
    JOURNAL OF VIBRATION AND CONTROL, 2009, 15 (09) : 1349 - 1363
  • [7] Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals
    Hwang, Don-Ha
    Youn, Young-Woo
    Sun, Jong-Ho
    Choi, Kyeong-Ho
    Lee, Jong-Ho
    Kim, Yong-Hwa
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2015, 10 (04) : 1558 - 1565
  • [8] Location of Defective Bearing in three-phase Induction Motor using Stockwell Transform and Support Vector Machine
    Singh, Megha
    Shaik, Abdul Gafoor
    2018 2ND INTERNATIONAL CONFERENCE ON POWER, ENERGY AND ENVIRONMENT: TOWARDS SMART TECHNOLOGY (ICEPE), 2018,
  • [9] Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform
    Heidari, Mohammad
    Homaei, Hadi
    Golestanian, Hossein
    Heidari, Ali
    JOURNAL OF VIBROENGINEERING, 2016, 18 (02) : 860 - 875
  • [10] Bearing Fault Detection in Three-Phase Induction Motors Using Support Vector Machine and Fiber Bragg Grating
    Brusamarello, Beatriz
    da Silva, Jean Carlos Cardozo
    Sousa, Kleiton de Morais
    Guarneri, Giovanni Alfredo
    IEEE SENSORS JOURNAL, 2023, 23 (05) : 4413 - 4421