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
  • [21] A New Fault Classification Scheme Using Vibration Signal Signatures and the Mahalanobis Distance
    Kim, Jaeyoung
    Hung Nguyen Ngoc
    Kim, Jongmyon
    INTEGRATED UNCERTAINTY IN KNOWLEDGE MODELLING AND DECISION MAKING, IUKM 2016, 2016, 9978 : 230 - 241
  • [22] Hybrid wavelet analysis of vibration signal singularity for condition monitoring and fault prognosis
    He, Zhengjia
    Jiang, Hongkai
    Zi, Yanyang
    WMSCI 2005: 9TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL 6, 2005, : 85 - 90
  • [23] Fault Signal Propagation in a Network of Distributed Motors
    Altaf, Saud
    Al-Anbuky, Adnan
    Hosseini, Hamid Gholam
    2014 IEEE 8TH INTERNATIONAL POWER ENGINEERING AND OPTIMIZATION CONFERENCE (PEOCO), 2014, : 59 - 63
  • [24] Bearing Fault Detection and Classification Using ANC-Based Filtered Vibration Signal
    Sahoo, Sudarsan
    Das, Jitendra Kumar
    ICCCE 2018, 2019, 500 : 325 - 334
  • [25] Research on fault diagnosis of rigid guide in hoist system based on vibration signal classification
    Lu, Xiang
    Liu, Zenghao
    Shen, Yucan
    Zhang, Fan
    Ma, Ning
    Hao, Haifei
    Liang, Zhen
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (11)
  • [26] A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring
    Gyftakis, Konstantinos N.
    Spyropoulos, Dionysios V.
    Kappatou, Joya C.
    Mitronikas, Epaminondas D.
    IEEE TRANSACTIONS ON ENERGY CONVERSION, 2013, 28 (02) : 267 - 277
  • [27] Wind Turbine Gearbox Vibration Signal Signature and Fault Development Through Time
    Koukoura, Sofia
    Carroll, James
    Weiss, Stepha
    McDonald, Alasdair
    2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2017, : 1380 - 1384
  • [28] Temperature Estimation and Vibration Monitoring for Induction Motors
    Liang, Xiaodong
    2017 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2017, : 198 - 203
  • [29] Vibration Analysis for Bearing Fault Detection in Electrical Motors
    Chaudhari, Yogita K.
    Gaikwad, Jitendra A.
    Kulkami, Jayant V.
    2014 FIRST INTERNATIONAL CONFERENCE ON NETWORKS & SOFT COMPUTING (ICNSC), 2014, : 146 - 150
  • [30] Fault Detection of the Electrical Motors Based on Vibration Analysis
    Agoston, Katalin
    8TH INTERNATIONAL CONFERENCE INTERDISCIPLINARITY IN ENGINEERING, INTER-ENG 2014, 2015, 19 : 547 - 553