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Bearing Fault Diagnosis Using an Extended Variable Structure Feedback Linearization Observer.

Farzin Piltan1, Jong-Myon Kim2

  • 1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 680⁻749, Korea. piltan_f@iranssp.org.

Sensors (Basel, Switzerland)
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A new variable structure feedback linearization observer (FLO) enhances bearing fault detection and diagnosis (FDD). This robust method improves fault estimation, ensuring safer rotating machinery operations.

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bearing fault detectionfeedback linearization observermodel-reference fault diagnosisproportional integral observervariable structure observer

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Area of Science:

  • Mechanical Engineering
  • Control Systems Engineering

Background:

  • Rolling element bearings are critical components in rotating machinery.
  • Effective bearing fault detection and diagnosis (FDD) is essential for operational safety and health.

Purpose of the Study:

  • To propose an enhanced observer-based FDD method using a variable structure feedback linearization observer (FLO).
  • To improve the robustness and reduce uncertainties in traditional feedback linearization observers for bearing fault diagnosis.

Main Methods:

  • Development of a variable structure feedback linearization observer (FLO).
  • Implementation of the FLO for fault estimation in rolling element bearings.
  • Validation using the Case Western University vibration dataset for outer, inner, and ball faults.

Main Results:

  • The proposed FLO demonstrated improved robustness in fault estimation compared to traditional methods.
  • The FLO outperformed the variable structure observer (VSO) by 5.5%, traditional feedback linearization observer (TFLO) by 8.5%, and proportional-integral observer (PIO) by 18.5% on average.
  • Successful identification of outer, inner, and ball bearing faults was achieved.

Conclusions:

  • The variable structure FLO is an effective and robust method for bearing fault detection and diagnosis.
  • This advanced observer technique enhances the reliability of rotating machinery monitoring systems.