摘要
针对埋地管道不同程度堵塞检测困难,且堵塞特征难以有效提取的问题,提出离散小波变换、信息增益、声压级变换、梅尔频率倒谱系数、极限学习机相结合的堵塞检测方法。首先,采用低频声学信号作为激励信号,采集待检测管道接收端的声压信息,获取相应的响应信号,对响应信号进行6层离散小波变换,得到8个不同频率范围的分解分量;其次,引入信息增益定量表征8个分量,筛选出包含管道运行状态信息最多的分量,最大限度的保留有效特征信息,对筛选后的分量信号进行声压级变换,提取梅尔频率倒谱系数构成特征向量;最后,利用极限学习机结构简单、学习速度快的特点,对管道运行状态进行有效识别,通过以上方式达到识别管道不同程度堵塞的目的。实验结果表明,该方法不仅能有效识别运行状态下管道的堵塞程度,而且能够排除三通件等常规部件对堵塞识别的影响,提高识别精度,对管道的正常运行和堵塞识别具有理论指导意义和应用价值。
Regarding the difficulties of detecting the different degrees of blocking in buried sewers and efficiently extracting effective blockage characteristics,a blocking detection method using acoustical characteristics based on information gain and extreme learning machine is proposed in this paper. Firstly,a sinusoidal sweep signal is used as the excitation signal,and the sound pressure data are collected at the receiving end to measure the acoustic impulse response of the pipe. Then a six-level wavelet decomposition tree is applied to obtain eight decomposed acoustic signal components in different frequency bands. After that,the information gain is introduced to quantitatively characterize the eight components,and the components that contain the most information about the pipe conditions are chosen so that the effective feature information can be kept. The sound pressure level is calculated to form the eigenvectors from the Mel-frequency cepstrum coefficients. Finally,the extreme learning machine is adopted due to its advantages of simple structure and fast learning speed. So,the pipe conditions can be effectively identified,as well as the different degrees of blocking of the pipe through the above methods,that the experimental results verify the effectiveness. Furthermore,it can also eliminate the interference of pipe components such as lateral connection to improve the accuracy of blockage identification,which has both theoretical significance and application value for condition assessment in sewer system.
作者
朱雪峰
冯早
吴建德
马军
ZHU Xuefeng;FENG Zao;WU Jiande;MA Jun(Faculty of Information Engineering and 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)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2021年第2期267-274,410,共9页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(61563024,51765022)。
关键词
排水管道
堵塞识别
信息增益
声学特征
sewer
blockage detection
information gain
acoustic characteristics
