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Online Signal Denoising Using Adaptive Stochastic Resonance in Parallel Array and Its Application to Acoustic Emission Signals

Signal denoising has been significantly explored in various engineering disciplines. In particular, structural health monitoring applications generally aim to detect weak anomaly responses (including acoustic emission (AE)) generated by incipient damage, which are easily buried in noise. Among various approaches, stochastic resonance (SR) has been widely adopted for weak signal detection. While many advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR, it often requires detailed information about the original signal a priori, which is hardly assessed from signals overwhelmed by noise. This research presents a novel online signal denoising strategy by utilizing SR in a parallel array of bistable systems. The original noisy input with additionally applied noise is adaptively scaled, so that the total noise level matches the optimal level that is analytically predicted from a generalized model to robustly enhance signal denoising performance for a wide range of input amplitudes that are often not known in advance. Thus, without sophisticated post-processing procedures, the scaling factor is straightforwardly determined by the analytically estimated optimal noise level and the ambient noise level, which is one of the few quantities that can be reliably assessed from noisy signals in practice. Along with numerical investigations that demonstrate the operational principle and the effectiveness of the proposed strategy, experimental validation of denoising AE signals by employing a bistable Duffing circuit system exemplifies the promising potential of implementing the new approach for enhancing online signal denoising in practice.

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Work Title Online Signal Denoising Using Adaptive Stochastic Resonance in Parallel Array and Its Application to Acoustic Emission Signals
Access
Open Access
Creators
  1. Jinki Kim
  2. Ryan L. Harne
  3. K. W. Wang
Keyword
  1. Stochastic resonance
  2. Signal processing
  3. Denoising
  4. Weak signal detection
  5. Acoustic emission
  6. Duffing
  7. Bistable system
  8. Machinery noise
  9. Nonlinear vibration
  10. Random vibration
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Journal of Vibration and Acoustics
Publication Date October 20, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1115/1.4052639
Deposited July 19, 2022

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Version 1
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  • Created

    July 19, 2022 10:56 by Researcher Metadata Database

  • Added Kim_online_signal_denoising_adaptive_stochastic_resonance_parallel_arrays_acoustic_emission.pdf

    July 19, 2022 10:56 by Researcher Metadata Database

  • Added Creator Jinki Kim

    July 19, 2022 10:56 by Researcher Metadata Database

  • Added Creator Ryan Harne

    July 19, 2022 10:56 by Researcher Metadata Database

  • Added Creator Kon-Well Wang

    July 19, 2022 10:56 by Researcher Metadata Database

  • Published

    July 19, 2022 10:56 by Researcher Metadata Database

  • Updated Work Title, Keyword, Description Show Changes

    January 24, 2024 14:21 by avs5190

    Work Title
    • Online signal denoising using adaptive stochastic resonance in parallel arra
    • Online Signal Denoising Using Adaptive Stochastic Resonance in Parallel Array and Its Application to Acoustic Emission Signals
    Keyword
    • Stochastic resonance, Signal processing, Denoising, Weak signal detection, Acoustic emission, Duffing, Bistable system, Machinery noise, Nonlinear vibration, Random vibration
    Description
    • Paper at JVA
    • Signal denoising has been significantly explored in various engineering disciplines. In particular, structural health monitoring applications generally aim to detect weak anomaly responses (including acoustic emission (AE)) generated by incipient damage, which are easily buried in noise. Among various approaches, stochastic resonance (SR) has been widely adopted for weak signal detection. While many advancements have been focused on identifying useful information from the frequency domain by optimizing parameters in a post-processing environment to activate SR, it often requires detailed information about the original signal a priori, which is hardly assessed from signals overwhelmed by noise. This research presents a novel online signal denoising strategy by utilizing SR in a parallel array of bistable systems. The original noisy input with additionally applied noise is adaptively scaled, so that the total noise level matches the optimal level that is analytically predicted from a generalized model to robustly enhance signal denoising performance for a wide range of input amplitudes that are often not known in advance. Thus, without sophisticated post-processing procedures, the scaling factor is straightforwardly determined by the analytically estimated optimal noise level and the ambient noise level, which is one of the few quantities that can be reliably assessed from noisy signals in practice. Along with numerical investigations that demonstrate the operational principle and the effectiveness of the proposed strategy, experimental validation of denoising AE signals by employing a bistable Duffing circuit system exemplifies the promising potential of implementing the new approach for enhancing online signal denoising in practice.
  • Renamed Creator Ryan L. Harne Show Changes

    January 24, 2024 14:21 by avs5190

    • Ryan Harne
    • Ryan L. Harne
  • Renamed Creator K. W. Wang Show Changes

    January 24, 2024 14:21 by avs5190

    • Kon-Well Wang
    • K. W. Wang
  • Updated

    April 04, 2024 10:21 by [unknown user]