Operating Monitoring and Fault Types Classification for Motors through Vibration Signal

被引:4
|
作者
Jiang, Jheng-Lun [1 ]
Chang, Hong-Chan [2 ]
Kuo, Cheng-Chien [2 ]
机构
[1] Inst Nucl Energy Res, Nucl Instrumentat Div, Taoyuan 325, Taiwan
[2] Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei 106, Taiwan
关键词
operating monitoring; fault types classification; motor; vibration signal;
D O I
10.1109/IS3C.2016.26
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
An operating monitoring combined with fault types classification system for motors by vibration signal is proposed in this paper. The main purpose is to develop vibration detection as the core of the motor operating status analysis system, and uses international standards including both ISO 10816 and NEMA MG-1, together with spectrum analysis to assess the degree of risk in the operation state of different vibration characteristics. The proposed prototype was devised and verified through onsite experimentation. Four artificial types of faults are made based on the literature survey for the most common fault types of motors, including the turn-to-turn fault of a stator coil, rotor bar breaking, bearing outer race breakage, and eccentric misalignment. Comparing the results to commercial tools showed similar spectral characteristics. Moreover, the experimental results shows promising ability and feasibility for online detection of motor's abnormal operation which could greatly assist operation and maintenance personnel to reduce the probability of a major accident.
引用
收藏
页码:61 / 64
页数:4
相关论文
共 50 条
  • [31] Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions
    Stander, CJ
    Heyns, PS
    Schoombie, W
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2002, 16 (06) : 1005 - 1024
  • [32] Bearing Fault Monitoring by Comparison with Main Bearing Frequency Components Using Vibration Signal
    Nivesrangsan, Pornchai
    Jantarajirojkul, Dutsadee
    PROCEEDINGS OF 2018 5TH INTERNATIONAL CONFERENCE ON BUSINESS AND INDUSTRIAL RESEARCH (ICBIR): SMART TECHNOLOGY FOR NEXT GENERATION OF INFORMATION, ENGINEERING, BUSINESS AND SOCIAL SCIENCE, 2018, : 292 - 296
  • [33] Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain
    Do, Van Tuan
    Chong, Ui-Pil
    STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, 2011, 57 (09): : 655 - 666
  • [34] Approach signal for rotor fault detection in induction motors
    Kechida R.
    Menacer A.
    Talhaoui H.
    Journal of Failure Analysis and Prevention, 2013, 13 (03) : 346 - 352
  • [35] Condition Monitoring through Mining Fault Frequency from Machine Vibration Data
    Rashid, Md. Mamunur
    Gondal, Iqbal
    Kamruzzaman, Joarder
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
  • [36] Fault classification in helicopter vibration signals
    Garga, AK
    Elverson, BT
    Lang, DC
    AMERICAN HELICOPTER SOCIETY - 53RD ANNUAL FORUM PROCEEDINGS, VOLS 1 AND 2, 1997, : 1316 - 1323
  • [37] A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps
    Casoli, Paolo
    Pastori, Mirko
    Scolari, Fabio
    Rundo, Massimo
    ENERGIES, 2019, 12 (05)
  • [38] Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform
    Janjarasjitt, Suparerk
    SENSORS, 2025, 25 (03)
  • [39] Electric Motor Vibration Signal Classification Using Wigner-Ville Distribution for Fault Diagnosis
    Wu, Jian-Da
    Luo, Wen-Jun
    Yao, Kai-Chao
    SENSORS, 2025, 25 (04)
  • [40] Diagnostics of Rotating Machinery through Vibration Monitoring: Signal Processing and Pattern Analysis
    Daga, Alessandro Paolo
    Garibaldi, Luigi
    APPLIED SCIENCES-BASEL, 2024, 14 (20):