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A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors

Author

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  • Federico Gargiulo

    (Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Universitá di Napoli Federico II, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Annalisa Liccardo

    (Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Universitá di Napoli Federico II, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Rosario Schiano Lo Moriello

    (Dipartimento di Ingegneria Industriale, Universitá di Napoli Federico II, 80125 Naples, Italy
    These authors contributed equally to this work.)

Abstract

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; therefore, motor diagnostics is an issue that assumes great importance. To prevent their failures and face the considered service outages in a timely manner, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on an artificial neural network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminarily validated on a set of 28 electric motors, and its performance is compared with common state-of-the-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98% in identifying anomalous conditions of three-phase asynchronous motors.

Suggested Citation

  • Federico Gargiulo & Annalisa Liccardo & Rosario Schiano Lo Moriello, 2022. "A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors," Energies, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4407-:d:840856
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    References listed on IDEAS

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    1. Petr Kacor & Petr Bernat & Petr Moldrik, 2021. "Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors," Energies, MDPI, vol. 14(20), pages 1-23, October.
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    4. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    5. Leopoldo Angrisani & Francesco Bonavolontà & Annalisa Liccardo & Rosario Schiano Lo Moriello & Francesco Serino, 2018. "Smart Power Meters in Augmented Reality Environment for Electricity Consumption Awareness," Energies, MDPI, vol. 11(9), pages 1-17, September.
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    Cited by:

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