基于多尺度加权排列熵的管道泄漏检测
|
摘要
管道的泄漏检测对于物料长距离运输的安全至关重要。利用加权排列熵方法分析管道的压力时间序列,可提取压力时间序列的特征,通过判定所提取特征的变化,实现管道的泄漏检测。考虑到单尺度加权排列熵在反映压力信号复杂度方面的不足,提出了基于多尺度加权排列熵的管道泄漏检测方法。该方法采取移动窗口法,选取固定长度的压力序列作为子序列。计算该子序列的多尺度加权排列熵,从而判定管道的泄漏。最后,通过对管道泄漏实验装置的仿真试验,验证了所提算法的有效性。
Pipeline leak detection technique plays an important role in the safety of long-distance material transportation. The characteristics of the pressure time series can be extracted by using the weighted permutation entropy method. Therefore, the pipeline leak can be detected by observing the abrupt change of these characteristics. A novel pipeline leak detection method based on multi-scale weighted permutation entropy was proposed to deal with the complexity of the pressure time series. The maximum overlapping moving window method was adopted to select a fixed pressure sequence as the subsequence. Then, the multi-scale weighted permutation entropy of the chosen subsequence was calculated to determine the leakage. The experimental results demonstrate the capability and efficiency of the proposed method.
Pipeline leak detection technique plays an important role in the safety of long-distance material transportation. The characteristics of the pressure time series can be extracted by using the weighted permutation entropy method. Therefore, the pipeline leak can be detected by observing the abrupt change of these characteristics. A novel pipeline leak detection method based on multi-scale weighted permutation entropy was proposed to deal with the complexity of the pressure time series. The maximum overlapping moving window method was adopted to select a fixed pressure sequence as the subsequence. Then, the multi-scale weighted permutation entropy of the chosen subsequence was calculated to determine the leakage. The experimental results demonstrate the capability and efficiency of the proposed method.
引文
[1] Chen Y,Kuo T,Kao W,et al. An improved method of soilgas sampling for pipeline leak detection:flow model analysis and laboratory test[J]. Journal of Natural Gas Science and Engineering,2017,42:226-231.
[2] He G X,Liang Y T,Li Y S,et al. A method for simulating the entire leaking process and calculating the liquid leakage volume of a damaged pressurized pipeline[J]. Journal of Hazardous Materials,2017,332:19-32.
[3]方娜,陈国明,朱红卫,等.海底管道泄漏事故统计分析[J].油气储运,2014,33(1):99-103.
[4]李健,陈世利,黄新敬,等.长输油气管道泄漏监测与准实时检测技术综述[J].仪器仪表学报,2016,37(8):1747-1760.
[5] Lu W Q,Liang W,Zhang L B,et al. A novel noise reduction method applied in negative pressure wave for pipeline leakage localization[J]. Process Safety and Environmental Protection,2016.104:142-149.
[6] Zhang Y,Chen S,Li J,et al. Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter[J].Measurement,2014,49(1):382-389.
[7] Yu X C,Liang W,Zhang L B,et al. Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline[J]. Mechanical Systems and Signal Processing,2016,72/73:266-285.
[8]杨进,文玉梅,李平.基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J].仪器仪表学报,2009,30(2):272-279.
[9]孙洁娣,彭志涛,温江涛,等.基于总体局域均值分解及稀疏表示分类的天然气管道泄漏孔径识别[J].中国机械工程,2017,28(10):1202-1209.
[10] Ni L, Jiang J C,Pan Y,et al. Leak location of pipelines based on characteristic entropy[J]. Journal of Loss Prevention in the Process Industries,2014,30(3):24-36.
[11] Gao J F,Chen K Y,Wu P,et al. Leakage detection of pipeline based on weighted-permutation entropy[C]//201729th Chinese Control and Decision Conference. 2017:2820-2823.
[12] Bandt C,Pompe B. Permutation entropy:a natural complexity measure for time series[J]. Physical Review Letters,2002,88(17):174102.
[13] Cui D,Pu W,Liu J. A new EEG synchronization strength analysis method:S-estimator based normalized weighted-permutation mutual information[J]. Neural Networks,2016,82(C):30-38.
[14] Stostic T, Stostic B, Singh V P. Optimizing stream flow monitoring networks using joint permutation entropy[J]. Journal of Hydrology,2017,552:306-312.
[15] Xia J.Shang P,Wang J,Shi W. Permutation and weightedpermutation entropy analysis for the complexity of nonlinear time series[J]. Communications in Nonlinear Science&Numerical Simulation.2016,31(1/2/3):60-68.
[16]任静波,孙根正,陈冰,等.基于多尺度排列熵的铣削颤振在线监测方法[J]·机械工程学报,2015,51(9):27-30.
[17] Zheng J D,Pan H Y,Yang S B,et al. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2017,99:229-243.
[18] Fadlallah B, Chen B, Keil A, et al. Weighted-permutationentropy:a complexity measure for time series incorporating amplitude information[J]. Physical Review E, 2013,87(2):1647-1650.
[19] Aziz W,Arif M. Multiscale Permutation Entropy of Physiological Time Series[C]//International Multitopic Conference,2007:1-6.
[20]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.
[21]李立明,张雯柏,柴晓冬,等.多传感数据同步采集系统设计[J].测控技术,2018,37(7):109-113.
[22]崔旭伟.管道泄漏信号的特征提取方法研究[D].大庆:东北石油大学,2015.
[23]赵鸿图,刘芳侯,侯守明.基于改进阈值函数的分数阶小波电能信号去噪[J].测控技术,2017,36(8):20-25.
[2] He G X,Liang Y T,Li Y S,et al. A method for simulating the entire leaking process and calculating the liquid leakage volume of a damaged pressurized pipeline[J]. Journal of Hazardous Materials,2017,332:19-32.
[3]方娜,陈国明,朱红卫,等.海底管道泄漏事故统计分析[J].油气储运,2014,33(1):99-103.
[4]李健,陈世利,黄新敬,等.长输油气管道泄漏监测与准实时检测技术综述[J].仪器仪表学报,2016,37(8):1747-1760.
[5] Lu W Q,Liang W,Zhang L B,et al. A novel noise reduction method applied in negative pressure wave for pipeline leakage localization[J]. Process Safety and Environmental Protection,2016.104:142-149.
[6] Zhang Y,Chen S,Li J,et al. Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter[J].Measurement,2014,49(1):382-389.
[7] Yu X C,Liang W,Zhang L B,et al. Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline[J]. Mechanical Systems and Signal Processing,2016,72/73:266-285.
[8]杨进,文玉梅,李平.基于相关分析和近似熵的管道泄漏声信号特征提取及辨识方法[J].仪器仪表学报,2009,30(2):272-279.
[9]孙洁娣,彭志涛,温江涛,等.基于总体局域均值分解及稀疏表示分类的天然气管道泄漏孔径识别[J].中国机械工程,2017,28(10):1202-1209.
[10] Ni L, Jiang J C,Pan Y,et al. Leak location of pipelines based on characteristic entropy[J]. Journal of Loss Prevention in the Process Industries,2014,30(3):24-36.
[11] Gao J F,Chen K Y,Wu P,et al. Leakage detection of pipeline based on weighted-permutation entropy[C]//201729th Chinese Control and Decision Conference. 2017:2820-2823.
[12] Bandt C,Pompe B. Permutation entropy:a natural complexity measure for time series[J]. Physical Review Letters,2002,88(17):174102.
[13] Cui D,Pu W,Liu J. A new EEG synchronization strength analysis method:S-estimator based normalized weighted-permutation mutual information[J]. Neural Networks,2016,82(C):30-38.
[14] Stostic T, Stostic B, Singh V P. Optimizing stream flow monitoring networks using joint permutation entropy[J]. Journal of Hydrology,2017,552:306-312.
[15] Xia J.Shang P,Wang J,Shi W. Permutation and weightedpermutation entropy analysis for the complexity of nonlinear time series[J]. Communications in Nonlinear Science&Numerical Simulation.2016,31(1/2/3):60-68.
[16]任静波,孙根正,陈冰,等.基于多尺度排列熵的铣削颤振在线监测方法[J]·机械工程学报,2015,51(9):27-30.
[17] Zheng J D,Pan H Y,Yang S B,et al. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2017,99:229-243.
[18] Fadlallah B, Chen B, Keil A, et al. Weighted-permutationentropy:a complexity measure for time series incorporating amplitude information[J]. Physical Review E, 2013,87(2):1647-1650.
[19] Aziz W,Arif M. Multiscale Permutation Entropy of Physiological Time Series[C]//International Multitopic Conference,2007:1-6.
[20]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.
[21]李立明,张雯柏,柴晓冬,等.多传感数据同步采集系统设计[J].测控技术,2018,37(7):109-113.
[22]崔旭伟.管道泄漏信号的特征提取方法研究[D].大庆:东北石油大学,2015.
[23]赵鸿图,刘芳侯,侯守明.基于改进阈值函数的分数阶小波电能信号去噪[J].测控技术,2017,36(8):20-25.