Acoustic emission monitoring during hydrotesting of a mounded LPG storage vessel of petrochemical industry

Mukhopadhyay, C. K.; Haneef, T.K.; Jayakumar, T.; Devaraj, R.; Bar, H. N.; Metya, Avijit Kumar; Murthy, G V S; Parida, N

doi:10.3233/sfc-160194

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…With high sensitivity and short response time, AE online monitoring technology plays an important role in initial damage monitoring of structures. 9 Its applications have covered the fields of petrochemicals, 10 railways, 11 aerospace, 12 construction and materials, 13 etc., which provides a new way for research into fault diagnosis.…”

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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