Condition Monitoring and Fault Diagnosis Techniques of Electric Machines

被引:0
|
作者
Nitish [1 ]
Singh, Amit Kr [1 ]
机构
[1] Dr BR Ambedkar NIT Jalandhar, Instrumentat & Control Engn, Jalandhar, Punjab, India
关键词
electrical faults; mechanical faults; ANN; MCSA; BLDC; BLAC; PCA; SVM;
D O I
10.1109/rdcape47089.2019.8979045
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
From many decades electric motors are used in industries and to achieve better results in modern energy conversion industry the development and modernization of the electric motors is very necessary. Also, several types of motors are used in our day to day life for important services such as carriage, medical purpose, armed work, and to communicate. Therefore, it becomes very necessary to monitor the conditions of the motor continuously. However, due to the limited lifetime of a material, weakening of motor's components, contamination in parts, defects during manufacturing, or other damages during process, an electric motor can face serious problems. A sudden failure may lead to the loss of valuable human life or expensive machinery in the industry, which should be prohibited by precise spotting or continue monitoring of working condition of a motor. This paper presented a review on electrical and mechanical faults diagnosis methods used so far to improve the performance of motors and also helps to prevent the unnecessary replacement of motor's parts and suddenly shutdown the production unit.
引用
收藏
页码:594 / 599
页数:6
相关论文
共 50 条
  • [41] On-line Fault Detection & Diagnosis of Rotating Machines using Acoustic Emission Monitoring Techniques
    Elmaleeh, Mohammed A. A.
    Saad, N.
    Ahmed, N.
    Awan, M.
    ICIAS 2007: INTERNATIONAL CONFERENCE ON INTELLIGENT & ADVANCED SYSTEMS, VOLS 1-3, PROCEEDINGS, 2007, : 897 - +
  • [42] Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring
    Chommuangpuck, Prathan
    Wanglomklang, Thanasak
    Srisertpol, Jiraphon
    SYSTEMS SCIENCE & CONTROL ENGINEERING, 2021, 9 (01): : 290 - 303
  • [43] Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring
    Chommuangpuck, Prathan
    Wanglomklang, Thanasak
    Srisertpol, Jiraphon
    Systems Science and Control Engineering, 2021, 9 (01): : 290 - 303
  • [44] A review: multiplicative faults and model-based condition monitoring strategies for fault diagnosis in rotary machines
    Prabhat Kumar
    Rajiv Tiwari
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2023, 45
  • [45] A review: multiplicative faults and model-based condition monitoring strategies for fault diagnosis in rotary machines
    Kumar, Prabhat
    Tiwari, Rajiv
    JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2023, 45 (05)
  • [46] A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines
    Liu, Zijian
    Zhang, Pinjia
    He, Shan
    Huang, Jin
    ENERGIES, 2021, 14 (14)
  • [47] Bearing condition monitoring methods for electric machines: A general review
    Zhou, Wei
    Habetler, Thomas G.
    Harley, Ronald G.
    2007 IEEE INTERNATIONAL SYMPOSIUM ON DIAGNOSTICS FOR ELECTRIC MACHINES, POWER ELECTRONICS & DRIVES, 2007, : 11 - 14
  • [48] Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review
    Zamudio-Ramirez, Israel
    Alfredo Osornio-Rios, Roque
    Antonino-Daviu, Jose A.
    Razik, Hubert
    de Jesus Romero-Troncoso, Rene
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (05) : 2895 - 2908
  • [49] Industry 4.0 in maintenance: using condition monitoring in electric machines
    Domingues, Nuno
    2021 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATION (DASA), 2021,
  • [50] Condition Monitoring of a IC Engine Fault Diagnosis using Machine Learning and Neural Network Techniques
    Kumar, Naveen P.
    Sakthivel, G.
    Jagadeeshwaran, R.
    SaravanaKumar, D.
    2020 6TH IEEE INTERNATIONAL SYMPOSIUM ON SMART ELECTRONIC SYSTEMS (ISES 2020) (FORMERLY INIS), 2020, : 183 - 189