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Default Detection in a Back-to-Back Planetary Gearbox Through Current and Vibration Signals

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2018)

Abstract

Until the last century, vibration analyses have been the most used method in monitoring the rotating machinery. But nowadays the electromechanical interaction in machinery is being the new scientific trend, mainly for its accuracy and facility.

Several research papers have shown that investigations done on the motor can be used to describe the machines dynamic behaviour and give a deep overview of any anomaly.

Within this context, this work reports the impact of an introduced pitting in the sun of one of the gearboxes of a back-to-back planetary gearbox configuration on the stator current signal. The gearbox monitoring was investigated through analysing the stator current measured experimentally by clamp meter.

Later, the results presented in the frequency spectrum are obtained using Fast Fourier Transform (FFT) to distinguish the frequency of the defect and its harmonics besides to the mesh frequency which highlight the electromechanical coupling.

Finally, the stator current signals were followed by the spectrum of acceleration signals. These signals were registered using an accelerometer mounted on the fixed ring in order to validate results foreseen in the current spectrum.

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Acknowledgements

This work has been supported by project DPI2017-85390-P funded by the Spanish Ministry of Economy, Industry and Competitiveness and project PredictEA funded by SODERCAN, S.A. and the FEDER operative program.

Acknowledgment also to both the University of Cantabria and the University of Sfax.

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Correspondence to Safa Boudhraa .

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Boudhraa, S., Fernández del Rincón, A., Chaari, F., Haddar, M., Viadero Rueda, F. (2019). Default Detection in a Back-to-Back Planetary Gearbox Through Current and Vibration Signals. In: Fernandez Del Rincon, A., Viadero Rueda, F., Chaari, F., Zimroz, R., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2018. Applied Condition Monitoring, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-11220-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-11220-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11219-6

  • Online ISBN: 978-3-030-11220-2

  • eBook Packages: EngineeringEngineering (R0)

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