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Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Author

Listed:
  • Vito Mario Fico

    (Skylife Engineering, 41092 Seville, Spain)

  • Antonio Leopoldo Rodríguez Vázquez

    (Skylife Engineering, 41092 Seville, Spain)

  • María Ángeles Martín Prats

    (Escuela Técnica Superior de Ingeniería, Electronics Engineering Department, Universidad de Sevilla, 41092 Seville, Spain)

  • Franco Bernelli-Zazzera

    (Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy)

Abstract

In recent years, Brushless DC (BLDC) motors have been gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro-Mechanical Actuator (EMA). In this framework, the availability of fault detection tools suited to these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques have been developed to a sufficiently high Technology Readiness Level (TRL) to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tried to collect the techniques which are closest to possible implementation. To fill a gap identified in the current techniques, a partial demagnetisation detection method is proposed in this paper. This technique takes advantage of the asymmetries generated in the current by the missing magnetic flux to detect the failure. Simulations and laboratory experiments have been carried out to validate the idea, showing the potential and the easy implementation of the method. The results have been examined in detail and satisfactory conclusions have been drawn.

Suggested Citation

  • Vito Mario Fico & Antonio Leopoldo Rodríguez Vázquez & María Ángeles Martín Prats & Franco Bernelli-Zazzera, 2019. "Failure Detection by Signal Similarity Measurement of Brushless DC Motors," Energies, MDPI, vol. 12(7), pages 1-23, April.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1364-:d:221237
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    Citations

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    Cited by:

    1. Vito Mario Fico & María Ángeles Martín Prats & Carmelina Ierardi, 2020. "High Technology Readiness Level Techniques for Brushless Direct Current Motors Failures Detection: A Systematic Review," Energies, MDPI, vol. 13(7), pages 1-24, April.
    2. Krzysztof Tomczyk & Marek Sieja & Grzegorz Nowakowski, 2021. "Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines," Energies, MDPI, vol. 14(11), pages 1-15, May.
    3. Marcin Skora & Pawel Ewert & Czeslaw T. Kowalski, 2019. "Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors," Energies, MDPI, vol. 12(21), pages 1-19, November.

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