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Updated: Jul 5, 2025

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An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber

Qi Liu1,2, Yongchao Yu1,2, Boon Siew Han1,2

  • 1Schaeffler Hub for Advanced Research at NTU, 61 Nanyang Dr, ABN-B1b-11, Singapore 637460, Singapore.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary

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This summary is machine-generated.

This study introduces an improved spectral subtraction method to remove noise in fiber Bragg grating sensor systems. The new technique effectively eliminates both white Gaussian noise and multifrequency interference for better rotating machinery fault diagnosis.

Area of Science:

  • Mechanical Engineering
  • Sensor Technology
  • Signal Processing

Background:

  • Condition monitoring of rotating machinery relies on sensors like fiber Bragg gratings (FBGs).
  • Additive noise, including white Gaussian noise and multifrequency interference, degrades FBG sensor data quality.
  • Existing spectral subtraction methods struggle with multifrequency interference and preserving useful signal components.

Purpose of the Study:

  • To propose an improved spectral subtraction (ISS) method for enhanced denoising in FBG-based condition monitoring.
  • To address the limitations of standard spectral subtraction in eliminating complex noise.
  • To maintain signal integrity while removing noise for accurate fault diagnosis.

Main Methods:

  • Developed an ISS method utilizing a reference noise signal measured under no working load.
Keywords:
additive noisecondition monitoringfiber Bragg grating sensorsignal processingspectral subtraction

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  • Employed a sliding window approach to segment power spectrums into frequency bands.
  • Modified the spectral subtraction formula to improve noise estimation and signal preservation.

    • Main Results:

    • Simulation analysis and experimental validation using an FBG sensor test rig were conducted.
    • The ISS method demonstrated effectiveness in simultaneously removing white Gaussian noise and multifrequency interference.
    • The proposed method successfully preserved essential signal information crucial for fault diagnosis.

    Conclusions:

    • The ISS method offers a significant improvement over standard spectral subtraction for FBG sensor data.
    • This technique enhances the reliability of condition monitoring and fault diagnosis in rotating machinery.
    • The ISS method is effective and feasible for real-world applications requiring robust noise reduction.