Entropy-Based Technique for Denoising of Acoustic Emission Signals

Bogomolov, Denis; Burda, E. A.; Testoni, Nicola; Kudryavtseva, I. S.; Marchi, Luca De; Naumenko, A. P.; Marzani, Alessandro

doi:10.1007/978-3-031-07254-3_64

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The proposed method has better waveform adaptability and fault location accuracies. Bogomolov et al 20 proposed a method to extract the arrival time of AE signals based on entropy-based filtering. It realizes accurate fault location of square aluminum plate under noisy conditions.…”

Section: Introductionmentioning

confidence: 99%

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

Liu

Jin

Wang

et al. 2023

Structural Health Monitoring

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The weak fault acoustic emission (AE) signals collected in the actual operating conditions of the engine are often submerged in the strong background noise. This paper proposes a denoising method of AE signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the above problem. Firstly, the wavelet packet is used to decompose engine background noise signals and noise-containing fault AE signals to enhance the local analysis capability of the autoencoder. Then, the dataset of each frequency band after decomposition is created. Among them, background noise signals are regarded as normal datasets. Noise-containing fault signals are treated as outlier datasets. The difference between each frequency band of background noise signals and noise-containing fault signals is analyzed. The autoencoder model is trained, validated and tested for effectiveness. In addition, a comparison is made with other commonly used denoising methods. Four types of evaluation indexes are introduced to quantitatively assess various methods. Finally, the real engine background noise signals with different signal-to-noise ratio (SNR) are added to the fault AE signals to verify the robustness of the proposed AE-WPD method. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different SNR. This lays the foundation for engine structural condition monitoring and subsequent fault identification and localization.

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Section: Introductionmentioning

confidence: 99%

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

Liu

Jin

Wang

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Kundu reviews different source localization techniques [6], including their advantages and limitations. Existing localization techniques can be applied for acoustic source localization [7][8][9][10], defect detection [11][12][13], and spacecraft impact detection [14]. Additionally, seismic waves can be an effective method for locating the projectile impact point [15].…”

Section: Introductionmentioning

confidence: 99%

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival ^*

Bi¹,

Huang²,

Wang³

et al. 2023

Meas. Sci. Technol.

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In outdoor shooting range experiments, locating the projectile impact point is typically done through manual search, which can be time-consuming and costly. To improve experimental efficiency and reduce costs, this paper proposes a new approach for localizing the impact point using the dynamic mode decomposition (DMD) extension method and time difference of arrival (TDOA) based on seismic waves. The DMD extension method is applied to one-dimensional seismic signals. Additionally, diagonal averaging is applied to recover the reconstructed matrix, which could achieve better noise reduction than conventional singular value decomposition and empirical mode decomposition methods. Numerical simulations are then conducted to analyze the impact point positioning model, resulting in an absolute location error within 20 m in a 1000 m square area. The work demonstrates that the joint positioning algorithm combining on DMD and TDOA achieves high positioning accuracy, making it a promising tool for future experiments on outdoor shooting ranges.

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Cluster-based sensor selection framework for acoustic emission source localization in concrete

et al. 2023

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Entropy-Based Technique for Denoising of Acoustic Emission Signals

Cited by 3 publications

References 10 publications

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival ^*

Cluster-based sensor selection framework for acoustic emission source localization in concrete

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Entropy-Based Technique for Denoising of Acoustic Emission Signals

Cited by 3 publications

References 10 publications

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival *

Cluster-based sensor selection framework for acoustic emission source localization in concrete

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Product

Resources

About

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival ^*