变工况下管道堵塞识别的声纹模型研究  

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

作　　者：杨佳睿 冯早 朱雪峰 YANG Jiarui;FENG Zao;ZHU Xuefeng(Faculty of Information Engineering&Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[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

出　　处：《机械科学与技术》2023年第6期914-922,共9页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金项目(61563024)。

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。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.

关 键 词：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 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置]