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Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach

Author

Listed:
  • Hong-Chan Chang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da’an Dist, Taipei 10607, Taiwan)

  • Yu-Ming Jheng

    (Department of Electrical Engineering, National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da’an Dist, Taipei 10607, Taiwan)

  • Cheng-Chien Kuo

    (Department of Electrical Engineering, National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da’an Dist, Taipei 10607, Taiwan)

  • Yu-Min Hsueh

    (Department of Electrical Engineering, National Taiwan University of Science and Technology No.43, Keelung Rd., Sec.4, Da’an Dist, Taipei 10607, Taiwan)

Abstract

This study develops a condition monitoring system, which includes operating condition monitoring (OCM) and fault diagnosis analysis (FDA). The OCM uses a vibration detection approach based on the ISO 10816-1 and NEMA MG-1 international standards, and the FDA uses a vibration-electrical hybrid approach based on various indices. The system can acquire real-time vibration and electrical signals. Once an abnormal vibration has been detected by using OCM, the FDA is applied to classify the type of faults. Laboratory results indicate that the OCM can successfully diagnose induction motors healthy condition, and FDA can classify the various damages stator fault, rotor fault, bearing fault and eccentric fault. The FDA with the hybrid approach is more reliable than the traditional approach using electrical detection alone. The proposed condition monitoring system can provide simple and clear maintenance information to improve the reliability of motor operations.

Suggested Citation

  • Hong-Chan Chang & Yu-Ming Jheng & Cheng-Chien Kuo & Yu-Min Hsueh, 2019. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach," Energies, MDPI, vol. 12(8), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1471-:d:224023
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    Citations

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    Cited by:

    1. Minghui Wang & Yongxiang Xu & Jibin Zou, 2019. "Sliding-Mode-Observer-Based Open-Switch Diagnostic Method for Permanent Magnet Synchronous Motor Drive Connected with LC Filter," Energies, MDPI, vol. 12(17), pages 1-19, August.
    2. Isac Antônio dos Santos Areias & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Germano Lambert-Torres & Vitor Almeida Bernardes, 2019. "Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors," Energies, MDPI, vol. 12(21), pages 1-15, October.
    3. Marco Antonio Rodriguez-Blanco & Victor Golikov & René Osorio-Sánchez & Oleg Samovarov & Gerardo Ortiz-Torres & Rafael Sanchez-Lara & Jose Luis Vazquez-Avila, 2022. "Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations," Energies, MDPI, vol. 15(22), pages 1-19, November.
    4. Mlungisi Ntombela & Kabeya Musasa, 2023. "Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    5. Anand Krishnasarma & Seyed Jamaleddin Mostafavi Yazdi & Allan Taylor & Daniel Ludwigsen & Javad Baqersad, 2021. "Acoustic Signature Analysis and Sound Source Localization for a Three-Phase AC Induction Motor," Energies, MDPI, vol. 14(21), pages 1-14, November.
    6. Arkadiusz Duda & Maciej Sułowicz, 2020. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms," Energies, MDPI, vol. 13(14), pages 1-26, July.
    7. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.
    8. Wagner Fontes Godoy & Daniel Morinigo-Sotelo & Oscar Duque-Perez & Ivan Nunes da Silva & Alessandro Goedtel & Rodrigo Henrique Cunha Palácios, 2020. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors," Energies, MDPI, vol. 13(13), pages 1-17, July.
    9. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Carlos López-Torres & Antoni Garcia, 2019. "Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 12(14), pages 1-15, July.
    10. Arkadiusz Duda & Piotr Drozdowski, 2020. "Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis," Energies, MDPI, vol. 13(24), pages 1-25, December.

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