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Elevator Car Vibration Signal Denoising Method Based on CEEMD and Bilateral Filtering.

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This study introduces a novel method for cleaning noisy elevator vibration signals using complementary ensemble empirical mode decomposition (CEEMD) and bilateral filtering. The technique effectively reduces noise, improving elevator diagnostics.

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Area of Science:

  • Engineering
  • Signal Processing
  • Vibration Analysis

Background:

  • Elevator car vibration signals are crucial for monitoring operational status.
  • Noise in vibration signals complicates accurate diagnosis and analysis.
  • Existing denoising methods may not sufficiently address complex noise patterns.

Purpose of the Study:

  • To develop and validate a novel noise reduction technique for elevator car vibration signals.
  • To enhance the reliability of vibration-based elevator monitoring and diagnostic systems.
  • To address the challenge of noise interference in signal acquisition.

Main Methods:

  • Utilized Complementary Ensemble Empirical Mode Decomposition (CEEMD) to decompose vibration signals into intrinsic mode functions (IMFs).
  • Applied correlation coefficient analysis to identify and remove spurious IMFs.
  • Employed bilateral filtering to denoise noise-dominant IMFs.
  • Reconstructed the filtered IMFs to obtain the final denoised signal.

Main Results:

  • The proposed CEEMD and bilateral filtering method effectively reduced noise in elevator car vibration signals.
  • Simulation and practical application tests confirmed the method's efficacy.
  • The denoising process preserved essential signal characteristics while removing noise.

Conclusions:

  • The integrated CEEMD and bilateral filtering approach offers a robust solution for denoising elevator vibration data.
  • This method significantly improves the quality of vibration signals for diagnostic purposes.
  • Enhanced signal quality facilitates more accurate and reliable elevator condition monitoring.