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1.中国科学院上海应用物理研究所 上海 201800
2.中国科学院大学 北京 100049
JIANG Wentao, male, born in 1999, graduated from Kunming University of Science and Technology in 2021, doctoral student, focusing on accident prediction and risk analysis
YANG Qun, E-mail: yangqun@sinap.ac.cn
Received:29 September 2025,
Revised:2026-01-21,
Online First:29 July 2026,
Published:15 July 2026
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JIANG Wentao,YUE Xin,SUN Liangjie,et al.Fluid noise elimination in centrifugal pump vibration signals using the frequency domain spline adaptive filter[J]. NUCLEAR TECHNIQUES,2026,49(07):071115.
蒋文涛,岳鑫,孙亮洁等.基于FDSAF的离心泵振动信号流体噪声剔除方法研究[J].核技术,2026,49(07):071115. DOI: 10.3724/j.0253-3219.2026.hjs.49.250374. CSTR: 32193.14.hjs.CN31-1342/TL.2026.49.250374.
JIANG Wentao,YUE Xin,SUN Liangjie,et al.Fluid noise elimination in centrifugal pump vibration signals using the frequency domain spline adaptive filter[J]. NUCLEAR TECHNIQUES,2026,49(07):071115. DOI: 10.3724/j.0253-3219.2026.hjs.49.250374. CSTR: 32193.14.hjs.CN31-1342/TL.2026.49.250374.
实际的离心泵信号中包含复杂的流体激励噪声,给结构故障诊断带来困难。针对离心泵振动信号中流体噪声的抑制问题,提出一种结合计算流体力学(Computational Fluid Dynamics,CFD)与频域样条自适应滤波(Frequency Domain Spline Adaptive Filter,FDSAF)的降噪方法,并进行实验验证。首先计算分析离心泵内部特定监测点的压力脉动信号,根据分析结果确定了参考噪声源与振动测点,并在测点位置采集泵壳的振动加速度信号。然后将参考噪声源的压力脉动信号作为FDSAF滤波器的参考噪声输入,剔除实测振动信号中的流体激励噪声。结果表明:所提出的结合CFD与FDSAF的降噪方法能够有效抑制振动信号中的流体激励噪声,提高结构振动特征的辨识度,为离心泵故障诊断中的数据预处理提供了一种新途径。
Background
2
Vibration signals of centrifugal pumps are often contaminated by complex fluid-induced noise
which complicates structural fault diagnosis.
Purpose
2
This study aims to address the suppression of fluid-induced noise in centrifugal pump vibration signals by proposing and experimentally validating a noise reduction method that integrates computational fluid dynamics (CFD) with the frequency domain spline adaptive filter (FDSAF).
Methods
2
Firstly
vibration acceleration signals of the pump casing were collected under rated operating conditions using a centrifugal pump vibration test rig. Pressure pulsation signals in key regions inside the pump were then calculated and extracted through CFD simulations
and the correlation between vibration signals and fluid pressure pulsation signals was analyzed. Subsequently
pressure pulsation signals were employed as reference inputs for the FDSAF filter to selectively eliminate fluid-induced noise from the measured vibration signals. Finally
comparative analysis between simulation and experimental results was conducted to valid the effectiveness of the proposed method.
Results
2
Comparison results show that the abovementioned approach effectively suppresses fluid-induced noise in vibration signals
thereby enhancing the distinguishability of structural vibration features. After denoising
the power spectral energy near the rotational frequency (49.1 Hz) is increased by 1.85 times
while the energy near the blade-passing frequency (294.6 Hz) is reduced to 26.7% of its original value
with its amplitude decreased by 64.5%.
Conclusions
2
The proposed CFD-FDSAF-based method in this study provides a new pathway for data preprocessing in centrifugal pump fault diagnosis by enabling more reliable extraction of fault-related vibration features.
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