Analyze acoustic emission signals from moving bubbles by clustering method

WANG Xin; LI Xiaolei; LI Meihui; SANG Xunyuan; WANG Taiyang

doi:10.11949/j.issn.0438-1157.20171572

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Analyze acoustic emission signals from moving bubbles by clustering method

更新时间：2025-12-10

- Analyze acoustic emission signals from moving bubbles by clustering method
- CIESC Journal Issue 7, Pages: 2964-2971(2018)
- 作者机构：
  
  中国石油大学(华东)储运系, 山东省油气储运安全省级重点实验室,山东,青岛,266580
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20171572
  CLC： 359+.1
- Online First：05 July 2018，
  
  Published：05 July 2018
- 稿件说明：
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WANG Xin, LI Xiaolei, LI Meihui, et al. Analyze acoustic emission signals from moving bubbles by clustering method[J]. CIESC Journal, 2018, (7): 2964-2971.
DOI：

WANG Xin, LI Xiaolei, LI Meihui, et al. Analyze acoustic emission signals from moving bubbles by clustering method[J]. CIESC Journal, 2018, (7): 2964-2971. DOI： 10.11949/j.issn.0438-1157.20171572.

摘要

利用自行开发的采集处理程序采集3 mm喷嘴释放的连续气泡上升阶段的声发射信号，提取声信号波包并计算声信号参数。基于k-均值聚类分析方法对孔口气速1.42 m/s时的声信号波包进行分类处理，分析各分类声信号参数统计规律。通过快速傅里叶变换和小波分析方法分析各分类声信号的时频特征，结合气泡运动图像对声信号进行有效识别，研究了多个气速下的分类规律。结果表明，基于k-均值聚类分析的时频分析方法得到的各分类声信号具有一定的相似度，结合高速摄像技术，可以有效分析气泡上升运动声信号，识别气泡振荡、破碎、聚并等运动形态。

Abstract

A real time acquisition and processing program was developed to record acoustic emission (AE) signals

to extract AE wave packets

and to calculate AE parameters of continuous bubbles in their rising stages after released from a 3-mm-diametered nozzle. AE signal wave packets of 1.42 m/s gas velocity at orifice was classified by kmeans clustering analysis method. For each category

statistical characteristics of AE signal parameters was analyzed and time/frequency domains of AE signals were analyzed by fast Fourier transform and wavelet transform method. Identification of AE signals was effectively achieved with assistance of moving bubble images. Signal classification capability were further studied under different gas velocities. The results show that AE signals of all categories have somewhat similarity after processed by time frequency analysis and k-means clustering method

AE signals of moving bubbles in their rising stages can be efficiently studied in combination with high-speed image-capturing technique

and flow patterns of moving bubbles can be identified

including bubble oscillation

breakup

coalescence and so on.

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