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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MDPI AG
2025-03-01
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| author | Shuxun Li Qian Zhao Jinwei Liu Xuedong Zhang Jianjun Hou |
| author_facet | Shuxun Li Qian Zhao Jinwei Liu Xuedong Zhang Jianjun Hou |
| author_sort | Shuxun Li |
| collection | DOAJ |
| description | 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 <i>K</i> 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. |
| format | Article |
| id | doaj-art-a80283c135f544f28bb70c2b88015f01 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-a80283c135f544f28bb70c2b88015f012025-08-20T02:59:01ZengMDPI AGSensors1424-82202025-03-01255157310.3390/s25051573Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold FunctionShuxun Li0Qian Zhao1Jinwei Liu2Xuedong Zhang3Jianjun Hou4School of Petrochemical Technology, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Petrochemical Technology, Lanzhou University of Technology, Lanzhou 730050, ChinaMachinery Industry Pump Special Valve Engineering Research Center, Lanzhou 730050, ChinaMachinery Industry Pump Special Valve Engineering Research Center, Lanzhou 730050, ChinaSchool of Petrochemical Technology, Lanzhou University of Technology, Lanzhou 730050, ChinaThe 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 <i>K</i> 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.https://www.mdpi.com/1424-8220/25/5/1573steam trapsparrow optimization algorithmimproved threshold functionsignal-to-noise ratioroot-mean-square error |
| spellingShingle | Shuxun Li Qian Zhao Jinwei Liu Xuedong Zhang Jianjun Hou Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function Sensors steam trap sparrow optimization algorithm improved threshold function signal-to-noise ratio root-mean-square error |
| title | Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function |
| title_full | Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function |
| title_fullStr | Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function |
| title_full_unstemmed | Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function |
| title_short | Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function |
| title_sort | noise reduction of steam trap based on ssa vmd improved wavelet threshold function |
| topic | steam trap sparrow optimization algorithm improved threshold function signal-to-noise ratio root-mean-square error |
| url | https://www.mdpi.com/1424-8220/25/5/1573 |
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