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Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor-Bearing System.

Sensors (Basel, Switzerland)·2025
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A Hybrid Diagnostic Framework with Compensation Algorithms for Inherent Rotor Faults Using Rotor Experiments.

Shyh-Chin Huang1, Thanh-Trung Pham1, Trong-Du Nguyen2

  • 1Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid method to accurately diagnose rotor faults like imbalance and shaft-bow. It improves prognostics and health management (PHM) by identifying and compensating for inherent system faults.

Keywords:
hybrid approachinherent faultsrotor faults diagnosisself-compensationssimultaneous imbalance and shaft-bow

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Area of Science:

  • Engineering
  • Mechanical Vibrations
  • Prognostics and Health Management (PHM)

Background:

  • Rotor-bearing systems in engineering applications suffer from inherent faults like imbalance and shaft-bow.
  • Early identification of these faults is crucial for accurate prognostics and health management (PHM).
  • Undetected inherent faults can lead to significant deviations in fault diagnosis and compromise maintenance strategies.

Purpose of the Study:

  • To develop a hybrid diagnostic methodology for enhanced rotor fault diagnosis.
  • To improve the simultaneous identification and parameter estimation of imbalance and shaft-bow faults.
  • To address discrepancies between model predictions and experimental results caused by inherent faults.

Main Methods:

  • Integration of a physics-based model with neural network techniques.
  • Demonstration using a Jeffcott rotor model with simultaneous disk imbalance and shaft-bow.
  • Development and experimental verification of algorithms for inherent fault identification and compensation.

Main Results:

  • The hybrid methodology demonstrated superior capability for simultaneous fault identification.
  • Discrepancies between model predictions and experimental data were observed due to inherent faults.
  • Compensation for inherent faults significantly improved the accuracy of simultaneous diagnosis and parameter estimation.

Conclusions:

  • The proposed hybrid methodology effectively enhances rotor fault diagnosis.
  • Inherent fault identification and compensation are critical for accurate prognostic assessments.
  • The methodology is suitable for real-time monitoring systems in industrial rotating machinery.