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Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques.

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  • 1School of Mechanical Engineering, University of Campinas, Campinas 13083-970, Brazil.

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Summary

This study introduces a novel automated algorithm for extracting modal parameters in rotating machinery using Operational Modal Analysis (OMA). The new method accurately identifies frequencies and damping ratios, improving machine condition monitoring.

Keywords:
automated operational modal analysishierarchical clusteringhydrodynamic bearingsrolling bearingsrotating machinery

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Area of Science:

  • Mechanical Engineering
  • Vibration Analysis
  • Condition Monitoring

Background:

  • Modal parameters from vibration signals are crucial for machine condition monitoring.
  • Operational Modal Analysis (OMA) is widely used but faces challenges in rotating machinery due to system-specific characteristics.
  • Existing automated modal parameter extraction algorithms are often unsuitable for rotating machinery.

Purpose of the Study:

  • To develop and validate a new automated algorithm for modal parameter identification in rotating machinery using OMA.
  • To address the limitations of current algorithms in handling the unique dynamics of rotating systems.
  • To enhance the efficiency and accuracy of condition monitoring for rotating machinery.

Main Methods:

  • Development of a novel automated algorithm for modal parameter identification.
  • Application of the algorithm to two distinct datasets representing different systems and test conditions.
  • Utilizing Operational Modal Analysis (OMA) for extracting modal parameters from vibration signals.

Main Results:

  • The proposed algorithm accurately extracts frequencies and damping ratios from the stabilization diagram.
  • The method demonstrated suitability for both rotor and foundation components.
  • The algorithm requires only one user-defined parameter, simplifying its application.

Conclusions:

  • The novel automated algorithm is effective for accurate modal parameter identification in rotating machinery via OMA.
  • The technique shows robustness across different systems and operating conditions.
  • This advancement facilitates improved condition monitoring systems for rotating machinery.