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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Kernel regression residual decomposition-based synchroextracting transform to detect faults in mechanical systems.

Hui Liu1, Jiawei Xiang1

  • 1College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, PR China.

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|December 13, 2018
PubMed
Summary

A new method, one-level kernel regression residual decomposition (KRRD) combined with synchroextracting transform (SET), effectively extracts high-frequency fault information from mechanical vibrations. This technique enhances fault detection by overcoming low-frequency interference and noise.

Keywords:
Fault diagnosisGearKernel regression residual decompositionRolling element bearingSynchroextracting transform

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Mechanical component health monitoring relies on analyzing vibration signals.
  • Fault information is often present in high-frequency components but obscured by noise and low-frequency interference.

Purpose of the Study:

  • To develop a novel preprocessing technique for extracting high-frequency fault information from mechanical vibration signals.
  • To enhance the signal-to-noise ratio (SNR) for improved fault feature detection.

Main Methods:

  • A one-level kernel regression residual decomposition (KRRD) method was introduced to isolate high-frequency terms.
  • The extracted high-frequency signal was further purified using synchroextracting transform (SET).
  • Hilbert envelope analysis was applied to the purified signal for fault feature demodulation.

Main Results:

  • The proposed one-level KRRD-based SET method demonstrated superior performance in extracting fault information compared to empirical mode decomposition (EMD) and variational mode decomposition (VMD).
  • Numerical simulations and experimental investigations validated the effectiveness of the method.
  • The technique successfully increased the SNR and enabled accurate fault feature frequency demodulation.

Conclusions:

  • The one-level KRRD-based SET method is highly effective for detecting mechanical component faults.
  • This approach offers a significant improvement over existing methods for analyzing noisy and complex vibration signals.
  • The technique provides a reliable tool for condition monitoring and predictive maintenance.