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Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals

Author

Listed:
  • Mahfoud Bouzouidja

    (Laspi Roanne, Université Jean Monnet Saint Etienne, 42300 Roanne, France)

  • Moncef Soualhi

    (SUPMICROTECH, CNRS, Institut FEMTO-ST, Université de Franche-Comté, 25000 Besançon, France)

  • Abdenour Soualhi

    (Laspi Roanne, Université Jean Monnet Saint Etienne, 42300 Roanne, France)

  • Hubert Razik

    (Laboratoire Ampère, CNRS, UMR 50005, Université de Lyon, 69100 Villeurbanne, France)

Abstract

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels.

Suggested Citation

  • Mahfoud Bouzouidja & Moncef Soualhi & Abdenour Soualhi & Hubert Razik, 2024. "Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals," Energies, MDPI, vol. 17(3), pages 1-25, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:643-:d:1328859
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    References listed on IDEAS

    as
    1. Yasser Damine & Noureddine Bessous & Remus Pusca & Ahmed Chaouki Megherbi & Raphaël Romary & Salim Sbaa, 2023. "A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process," Energies, MDPI, vol. 16(6), pages 1-27, March.
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