Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function

The performance of steam traps plays an important role in the normal operation of steam systems. It also contributes to the improvement of thermal efficiency of steam-using equipment and the rational use of energy. As an important component of the steam system, it is crucial to monitor the state of...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 5; p. 1573
Main Authors: Li, Shuxun, Zhao, Qian, Liu, Jinwei, Zhang, Xuedong, Hou, Jianjun
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 04.03.2025
MDPI
Subjects:
Accuracy
Acoustic emission testing
Acoustics
Algorithms
Energy consumption
Fourier transforms
improved threshold function
Lagrange multiplier
Methods
Noise control
Optimization algorithms
root-mean-square error
Signal processing
signal-to-noise ratio
sparrow optimization algorithm
steam trap
Wavelet transforms
China
steam trap
root-mean-square error
improved threshold function
sparrow optimization algorithm
signal-to-noise ratio
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The performance of steam traps plays an important role in the normal operation of steam systems. It also contributes to the improvement of thermal efficiency of steam-using equipment and the rational use of energy. As an important component of the steam system, it is crucial to monitor the state of the steam trap and establish a correlation between the acoustic emission signal and the internal leakage state. However, in actual test environments, the acoustic emission sensor often collects various background noises alongside the valve internal leakage acoustic emission signal. Therefore, to minimize the impact of environmental noise on valve internal leakage identification, it is necessary to preprocess the original acoustic emission signals through noise reduction before identification. To address the above problems, a denoising method based on a sparrow search algorithm, variational modal decomposition, and improved wavelet thresholding is proposed. The sparrow search algorithm, using minimum envelope entropy as the fitness function, optimizes the decomposition level K and the penalty factor α of the variational modal decomposition algorithm. This removes modes with higher entropy in the modal envelopes. Subsequently, wavelet threshold denoising is applied to the remaining modes, and the denoised signal is reconstructed. Validation analysis demonstrates that the combination of SSA-VMD and the improved wavelet threshold function effectively filters out noise from the signal. Compared to traditional thresholding methods, this approach increases the signal-to-noise ratio and reduces the root-mean-square error, significantly enhancing the noise reduction effect on the steam trap’s background noise signal.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25051573