小波变换实现混沌雷达高精度泄漏检测研究
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摘要
利用超宽带电混沌信号的强抗干扰性和随机性,通过分析超宽带混沌信号和经管道反射后雷达回波信号的互相关函数以及峰值信噪比,对管道泄漏点进行定位。在接收端,通过对雷达回波信号的小波阈值去噪和数据分析,探究小波基sym4在3、4、5层小波分解之后的去噪效果,有效提高泄漏的检测精度。结果表明:小波基sym4,在加入5 d B高斯白噪声情况下,互相关系数方法中,采用4层分解的硬阈值法可以取得最佳去噪效果;小波基sym4,在加入5 d B高斯白噪声情况下,峰值比信噪比方法中,采用5层分解的硬阈值法可以取得最佳去噪效果,从而实现高精度泄漏检测。
A new method of pipeline leak detection and location is proposed.The cross-correlation function and the peak signal-tonoise ratio of the radar echo signal of the UWB signal chaotic signal is studied by using its strong anti-jamming and randomness. At the receiver,the wavelet threshold denoising and data analysis of the radar echo signal are used to investigate the denoising effect of the wavelet base sym4 in the 3,4,5 layer wavelet decomposition,and efficiently increase the detection precision of the pipeline leak. The results show that the wavelet base sym4 can obtain the best denoising effect by using the hard threshold method of 4-layer decomposition in the case of 5 d B Gaussian white noise. In the case of adding 5 d B Gaussian white noise,the peak signal to noise ratio method,the use of 5-layer decomposition of the hard threshold method can achieve the best denoising effect,in order to achieve higher accuracy.
A new method of pipeline leak detection and location is proposed.The cross-correlation function and the peak signal-tonoise ratio of the radar echo signal of the UWB signal chaotic signal is studied by using its strong anti-jamming and randomness. At the receiver,the wavelet threshold denoising and data analysis of the radar echo signal are used to investigate the denoising effect of the wavelet base sym4 in the 3,4,5 layer wavelet decomposition,and efficiently increase the detection precision of the pipeline leak. The results show that the wavelet base sym4 can obtain the best denoising effect by using the hard threshold method of 4-layer decomposition in the case of 5 d B Gaussian white noise. In the case of adding 5 d B Gaussian white noise,the peak signal to noise ratio method,the use of 5-layer decomposition of the hard threshold method can achieve the best denoising effect,in order to achieve higher accuracy.
引文
[1]ZADKARAMI M,SHAHBAZIAN M,SALAHSHOOR K.Pipeline leakage detection and isolation:An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neuralnetwork(MLPNN)[J].Journal of Loss Prevention in the Process Industries,2016(43):479-487.
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[14]周颖涛,周绍骑,晁文胜,等.基于HHT时-频熵的声发射管理泄漏诊断[J].油气储运,2016,35(3):250-253.ZHOU Yingtao,ZHOU Shaoqi,CHAO Wensheng,et al.Acoustic emission pipeline leakage diagnosis based on timefrequency entropy of HHT[J].Oil&Gas Storage and Transportation,2016,35(3):250-253.
[15]LI Suzhen,WANG Xinxin,ZHAO Ming.An improved crosscorrelation method based on wavelet transform and energy feature extraction for pipeline leak detection[J].Smart Structures and Systems,2015,16(1):213-222.
[16]郭凤霞,戚俊,陈斐楠,等.基于小波变换的声雷达模拟信号去噪研究[J].现代雷达,2016,38(3):86-90.GUO Fengxia,QI Jun,CHEN Feinan,et al.A study on noise suppression methods for sodar analog signals based on wavelet transform[J].Modern Radar,2016,38(3):86-90.
[17]ZADKARAMI M,SHAHBAZIAN M,SALAHSHOOR K.Pipeline leak diagnosis based on wavelet and statistical features using Dempster-Shafer classifier fusion technique[J].Process Safety and Environmental Protection,2017,105:156-163.
[2]文玉梅,张雪园,文静,等.依据声信号频率分布和复杂度的供水管道泄漏辨识[J].仪器仪表学报,2014,35(6):1223-1229.WEN Yumei,ZHANG Xueyuan,WEN Jing,et al.Identification of water pipeline leakage based on acoustic signal frequency distribution and complexity[J].Chinese Journal of Scientific Instrument,2014,35(6):1223-1229.
[3]MCRAE T G,DEWEY A H.Photo-acoustic leak detection system and method[P].US5161408.US,1992.
[4]RAHIM H A,HAMID K A,ABIDIN H Z.Early detection of pipeline leakage using ultrasonic sensor[J].Journal Teknologi Sciences&Engineering,2015,73(3):9-11.
[5]HAMEED A,MALHOTRA V.Detection of leaks from process pipes[J].Pipes and Pipelines International,1999,44(5):23-33.
[6]FENG X,WU W,LI X,et al.Experimental investigations on detecting lateral buckling for subsea pipelines with distributed fiber optic sensors[J].Smart Structures and Systems,2015,15(2):245-258.
[7]ELFORJANI M,MBA D.Detecting natural crack initiation and growth in slow speed shafts with the acoustic emission technology[J].Engineering Failure Analysis,2009,16(7):2121-2129.
[8]ZHANG J R,NIE D H,WANG C.Research on water pipeline leak location[J].Applied Mechanics and Materials,2012,226-228:2143-2146.
[9]LEE M R,LEE J H.Leak detection of the pipeline using acoustic emission technique[J].ASME/JSME 2004 Pressure Vessels and Piping Conference,2004,77(10):1665-1673.
[10]谭剑波,彭扬.风廓线雷达RASS声发射系统设计[J].现代雷达,2002,24(5):58-60.TAN Jianbo,PEMG Yang.Sound transmitter system of RASS in wind profile radar[J].Modern Radar,2002,24(5):58-60.
[11]SANG Y J,ZHANG J,LU X,et al.Signal processing based on wavelet transform in pipeline leakage detection and location[C]//Sixth International Conference on IEEE Intelligent Systems Design and Applications.Jinan,China:IEEE Press,2006:734-739.
[12]GHAZALI M F,STASZEWSKI W J,SHUCKSMITH J,et al.Instantaneous phase and frequency for the detection of leaks and geatures in a pipeline system[J].Opec Review,2011,19(1):37-44.
[13]刘贵杰,徐萌,王欣,等.基于HHT的管道阀门内漏声发射检测研究[J].振动与冲击,2012,31(23):62-66.LIU Guijie,XU Meng,WANG Xin,et al.AE detection for pipeline valve leaklage based on HHT[J].Journal of Vibration and Shock,2012,31(23):62-66.
[14]周颖涛,周绍骑,晁文胜,等.基于HHT时-频熵的声发射管理泄漏诊断[J].油气储运,2016,35(3):250-253.ZHOU Yingtao,ZHOU Shaoqi,CHAO Wensheng,et al.Acoustic emission pipeline leakage diagnosis based on timefrequency entropy of HHT[J].Oil&Gas Storage and Transportation,2016,35(3):250-253.
[15]LI Suzhen,WANG Xinxin,ZHAO Ming.An improved crosscorrelation method based on wavelet transform and energy feature extraction for pipeline leak detection[J].Smart Structures and Systems,2015,16(1):213-222.
[16]郭凤霞,戚俊,陈斐楠,等.基于小波变换的声雷达模拟信号去噪研究[J].现代雷达,2016,38(3):86-90.GUO Fengxia,QI Jun,CHEN Feinan,et al.A study on noise suppression methods for sodar analog signals based on wavelet transform[J].Modern Radar,2016,38(3):86-90.
[17]ZADKARAMI M,SHAHBAZIAN M,SALAHSHOOR K.Pipeline leak diagnosis based on wavelet and statistical features using Dempster-Shafer classifier fusion technique[J].Process Safety and Environmental Protection,2017,105:156-163.