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Evaluation of Current Signature in Bearing Defects by Envelope Analysis of the Vibration in Induction Motors

Author

Listed:
  • Isac Antônio dos Santos Areias

    (Institute of System Engineering and Information Technology, Itajuba Federal University, Itajuba 37500-903, Brazil)

  • Luiz Eduardo Borges da Silva

    (Institute of System Engineering and Information Technology, Itajuba Federal University, Itajuba 37500-903, Brazil)

  • Erik Leandro Bonaldi

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Levy Ely de Lacerda de Oliveira

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Germano Lambert-Torres

    (Gnarus Institute, Itajuba 37500-052, Brazil)

  • Vitor Almeida Bernardes

    (Norte Energia, Altamira 70760-776, Brazil)

Abstract

Motor current signature analysis (MCSA) enables non-invasive monitoring, without interruption of machine operation in a remote and online way, allowing the identification of various types of faults of electrical and mechanical nature without the need of accessing the motor itself, but only its supply cables. Despite its advantages, it has limitations in accurately diagnosing incipient roller bearing faults. For the detection of incipient roller bearing faults, envelope analysis of vibration signals is a well-known and stablished technique used by motor condition monitoring experts for a long time, overcoming MCSA for that purpose. Thus, it is proposed in this paper, that the fault characteristic frequencies of roller bearings are identified in the current spectrum with the aid of envelope analysis on the bearing vibration signal. After this aided identification, the fault related spectral components in the current spectrum can be correctly tracked over time for trending evaluation and decision-making. This approach can represent a significant economic value in a motor condition monitoring program, since vibration envelope analysis is performed only at a first step and, after that, its results can be applied for the MCSA monitoring of all same-model motor drivers in an industrial site. This approach is even more valuable considering the concept of the Self-Supplied Wireless Current Transducer (SSWCT) also proposed in this paper. The SSWCT is an Industrial Internet of Things (IIOT) device for MCSA application in an Industry 4.0 environment. This proposed device has wireless communication interface and wireless/battery less power supply, being supplied by the energy harvested from the magnetic field of the same currents it is transducing. So, it is a completely galvanic isolated monitoring device, without batteries and without any electric connections to the industry electric system, easily installable to the motor cables, not using precious space in the electric panels of the motor control centers and not having any physical contact to the monitored asset.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4029-:d:279325
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    References listed on IDEAS

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    1. Lei Fu & Tiantian Zhu & Kai Zhu & Yiling Yang, 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy," Energies, MDPI, vol. 12(16), pages 1-20, August.
    2. 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.
    3. Camila Paes Salomon & Claudio Ferreira & Wilson Cesar Sant’Ana & Germano Lambert-Torres & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Bruno Silva Torres, 2019. "A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems," Energies, MDPI, vol. 12(8), pages 1-16, April.
    4. Xiaochuan Li & Faris Elasha & Suliman Shanbr & David Mba, 2019. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning," Energies, MDPI, vol. 12(14), pages 1-17, July.
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    Cited by:

    1. Zhiyong Zhou & Junzhong Sun & Wei Cai & Wen Liu, 2023. "Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors," Energies, MDPI, vol. 16(2), pages 1-13, January.
    2. 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.
    3. 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.
    4. Rafał Trzaska & Adam Sulich & Michał Organa & Jerzy Niemczyk & Bartosz Jasiński, 2021. "Digitalization Business Strategies in Energy Sector: Solving Problems with Uncertainty under Industry 4.0 Conditions," Energies, MDPI, vol. 14(23), pages 1-21, November.
    5. 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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