变工况下管道堵塞识别的声纹模型研究

杨佳睿; 冯早; 朱雪峰

doi:10.13433/j.cnki.1003-8728.20220037

变工况下管道堵塞识别的声纹模型研究

doi: 10.13433/j.cnki.1003-8728.20220037

杨佳睿^{1, 2,},
冯早^{1, 2, ,},
朱雪峰^{1, 2}

1.
昆明理工大学信息工程与自动化学院，昆明　650500
2.
昆明理工大学云南省人工智能重点实验室，昆明　650500

基金项目: 国家自然科学基金项目（61563024）

详细信息

作者简介:
杨佳睿（1997−），硕士研究生，研究方向为信号处理、模式识别，1184191884@qq.com

通讯作者:
冯早，副教授，硕士生导师，6483975@qq.com

中图分类号: TP274.2
计量
- 文章访问数: 51
- HTML全文浏览量: 23
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-02
- 刊出日期: 2023-06-25

Research on Voiceprint Model of Pipeline Blockage Recognition Under Variable Working Conditions

YANG Jiarui^{1, 2
,},
FENG Zao^{1, 2
, ,},
ZHU Xuefeng^{1, 2}

1.
Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China
2.
Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China

摘要

摘要: 针对排水管道堵塞检测实际应用中训练样本所包含的工况类别受限导致新工况识别率低下的问题，提出一种基于精细复合多尺度散布熵（Refined composite multi-scale dispersion entropy，RCMDE）和高斯混合隐马尔可夫模型（Gaussian-mixed-model hidden Markov model，GMM-HMM）的管道堵塞声纹识别模型。首先，采用基于子带谱熵的单参数双门限端点检测算法对单一和复杂工况下降噪后整体声压信号进行端点检测和信号分割，得到对应管道内堵塞物、三通件和管道尾端的个体声压信号。然后，提取精细复合多尺度散布熵作为特征向量。最后，将单一工况下不同类别的声压信号的特征向量用于模型训练，训练好的模型用于复杂工况下的堵塞物、三通件以及管道尾端的识别。结果表明：所提出的声纹识别模型在训练样本工况类别受限的条件下能有效识别变工况下排水管道中的堵塞物，三通件以及管道尾端,综合识别率为93.75%。验证在不同工况下堵塞物对声波的影响具有共性，与三通件、管道尾端具有差异性，具有一定的工程应用价值。
- 排水管道 /
- 变工况 /
- 故障诊断 /
- 端点检测 /
- 精细复合多尺度散布熵 /
- GMM-HMM模型
Abstract: In order to solve the problem that the recognition rate of new working conditions is low due to the limited working conditions category contained in the training samples in the practical application of drainage pipe blockage detection, a new voice print recognition model based on Refined Composite Multi-scale Dispersion Entropy and Gaussian Mixture Hidden Markov Model is proposed. Firstly, this method uses single-parameter dual-threshold endpoint detection algorithm based on sub-band spectral entropy to perform endpoint detection and signal segmentation for the sound pressure signal after noise reduction in single and complicated working conditions, and obtain sound pressure signals corresponding to blockage, lateral connection and pipe end in the pipeline. Then the refined composite multi-scale dispersion entropy features are extracted. Finally, the feature vectors of different types of individual sound pressure signals in a single working condition are used for model training, and the trained parameter model used for complex working conditions recognition of blockages, lateral connection and pipe end. The experimental results have shown that the proposed method can effectively identify blockages, pipe fittings such as lateral connection and pipe end under the condition of limited training sample categories, the comprehensive recognition rate is 93.75%, which verified that the voiceprint of the blockage has commonality under different working conditions, and it is different from lateral connection and pipe end and has certain engineering application value.
- drainage pipe /
- variable condition /
- fault diagnosis /
- endpoint detection /
- refined composite multi-scale dispersion entropy /
- GMM-HMM model

HTML全文

图 1 模型识别流程

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图 2 实验平台

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图 3 堵塞物和三通件实物图

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图 4 声压信号数据采集实验平台简图

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图 5 55 mm堵塞物声压信号降噪和端点检测

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图 6 声压信号的RCMDE均值

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图 7 声压信号的MDE均值

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图 8 声压信号的MSE均值

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图 9 不同类型的声压信号在各GMM-HMM模型下的对数似然概率输出值

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表 1 模型识别正确率对比

信号类型	测试样本数量	MSE-GMM-HMM/%	MDE-GMM-HMM/%	RCMDE-GMM-HMM
55 mm 堵塞物	20	75	80	90
40 mm堵塞物	20	80	85	90
三通件	20	80	80	95
管道尾端	20	100	100	100
平均识别率	20	83.75	86.25	93.75

下载: 导出CSV

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