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Research on the Noise Reduction Method of the Vibration Signal of the Hydrogenerator Unit Based on ITD-PE-SVD
Mathematical Problems in Engineering Pub Date : 2021-09-08 , DOI: 10.1155/2021/9589412
Yan Ren 1, 2 , Pan Liu 1 , Leiming Hu 3 , Ruoyu Qiao 1 , Linlin Zhang 1 , Shaojie Huang 4
Affiliation  

Aiming at the problem that the vibration signals of the hydrogenerator unit are nonlinear and nonstationary and it is difficult to extract the signal features due to strong background noise and complex electromagnetic interference, this paper proposes a dual noise reduction method based on intrinsic time-scale decomposition (ITD) and permutation entropy (PE) combined with singular value decomposition (SVD). Firstly, the vibration signals are decomposed by ITD to obtain a series of PRC components, and the permutation entropy of each component is calculated. Secondly, according to the set permutation entropy threshold, the PRC components are selected for reconstruction to achieve a noise reduction effect. On this basis, SVD is carried out, and the appropriate reconstruction order is selected according to the position of the singular value difference spectrum mutation point for reconstruction, so as to achieve the secondary noise reduction effect. The proposed method is compared with the LMD-PE-SVD and EMD-PE-SVD dual noise reduction method by simulation, taking the correlation coefficient and signal-to-noise ratio to evaluate the noise reduction performance and finding that the ITD-PE-SVD noise reduction has good noise reduction and pulse effect. Furthermore, this method is applied to the analysis of the upper guide swing data in the X-direction and Y-direction of a unit in a hydropower station in China, and it is found that this method can effectively reduce noise and accurately extract signal features, thus determining the vibration cause, which is helpful to improve the turbine fault recognition rate.

中文翻译:

基于ITD-PE-SVD的水轮发电机组振动信号降噪方法研究

针对水轮发电机组振动信号非线性、非平稳,背景噪声强,电磁干扰复杂,难以提取信号特征的问题,提出一种基于固有时标分解的对偶降噪方法。 (ITD) 和置换熵 (PE) 结合奇异值分解 (SVD)。首先,通过ITD对振动信号进行分解,得到一系列PRC分量,并计算每个分量的排列熵。其次,根据设置的置换熵阈值,选择PRC分量进行重构,达到降噪效果。在此基础上进行SVD​​,根据奇异值差分谱变异点的位置选择合适的重构阶次进行重构,以达到二次降噪的效果。通过仿真将所提方法与LMD-PE-SVD和EMD-PE-SVD双重降噪方法进行比较,以相关系数和信噪比来评价降噪性能,发现ITD-PE- SVD降噪具有良好的降噪和脉冲效果。此外,该方法应用于分析上导杆摆动数据。以相关系数和信噪比来评价降噪性能,发现ITD-PE-SVD降噪具有良好的降噪和脉冲效果。此外,该方法应用于分析上导杆摆动数据。以相关系数和信噪比来评价降噪性能,发现ITD-PE-SVD降噪具有良好的降噪和脉冲效果。此外,该方法应用于分析上导杆摆动数据。我国某水电站机组的X向Y向,发现该方法能有效降低噪声,准确提取信号特征,从而确定振动原因,有助于提高汽轮机故障识别率.
更新日期:2021-09-08
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