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Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

Author

Listed:
  • Christian Gianoglio

    (Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy)

  • Edoardo Ragusa

    (Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy)

  • Paolo Gastaldo

    (Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy)

  • Federico Gallesi

    (Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy)

  • Francesco Guastavino

    (Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy)

Abstract

Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.

Suggested Citation

  • Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4711-:d:607755
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    References listed on IDEAS

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    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    2. Salameh, F. & Picot, A. & Chabert, M. & Maussion, P., 2017. "Regression methods for improved lifespan modeling of low voltage machine insulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 200-216.
    3. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    4. Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.
    5. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    6. Christian Gianoglio & Edoardo Ragusa & Andrea Bruzzone & Paolo Gastaldo & Rodolfo Zunino & Francesco Guastavino, 2020. "Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus," Energies, MDPI, vol. 13(5), pages 1-16, March.
    7. Vanessa Neves Höpner & Volmir Eugênio Wilhelm, 2021. "Insulation Life Span of Low-Voltage Electric Motors—A Survey," Energies, MDPI, vol. 14(6), pages 1-32, March.
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    Cited by:

    1. Arshiah Yusuf Mirza & Ali Bazzi & Hiep Hoang Nguyen & Yang Cao, 2022. "Motor Stator Insulation Stress Due to Multilevel Inverter Voltage Output Levels and Power Quality," Energies, MDPI, vol. 15(11), pages 1-18, June.
    2. Sonia Ait-Amar & Abdoulay Koita & Gabriel Vélu, 2022. "Interpretation of Eccentricity of an Enameled Wire by Capacitance Measurements," Energies, MDPI, vol. 15(8), pages 1-9, April.
    3. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.

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