小波分析是目前管道泄漏检测中应用极为广泛的降噪方法，但使用的均为通用小波基函数，不能有效匹配具体的管道泄漏声发射信号，从而降低了降噪效果。针对该问题，本文首先通过数学解析模型计算得到管道泄漏声发射仿真信号，再以此信号为基础，构造出了适合管道泄漏特征的小波基函数。利用构造的小波基对实验泄漏信号进行降噪并做互相关计算，结果表明当小波消失矩为3或4、滤波器支撑长度为8时的泄漏定位误差最小，仅为3.62%。将此构造小波基与通用小波基系列分别对实验泄漏信号进行降噪并做互相关计算，结果表明采用构造小波基时的泄漏定位误差是最小的。这说明该小波基符合管道泄漏声发射信号的基本特征，从而可以更有效地抑制噪声，降低管道泄漏定位的误差。
Abstract
As a kind of noise reduction method, wavelet analysis is universally applied in test of pipe leakage. But the wavelet basis functions used are almost general ones which can’t effectively match specific acoustic emission signals of pipe leakage, thereby lowering effects of noise reducing. Aiming at this problem, analogue acoustic emission signals are firstly obtained theoretically by calculation through mathematical analytical model, then based on the analogue signals, wavelet basis suited to the character of pipe leakage is constructed. Utilizing constructed wavelet to reduce the noises of experiment signals and then through cross correlation calculation, the results show that the wavelet basis with vanish moment 3 or 4 and filter support length 8 minimizes the error of leakage localization, only 3.62%. The experimental leakage signals are denoised and cross-correlated by using the constructed wavelet basis and the universal wavelet basis. The results show that the leakage location error is minimum when using the constructed wavelet basis. It shows that the constructed wavelet basis conforms to the basic characteristics of acoustic emission signals in pipe leakage, thereby more effectively suppress noise and reduce the location error of pipe leakage.
关键词
管道 /
泄漏 /
声发射 /
小波基
{{custom_keyword}} /
Key words
pipe /
leakage /
acoustic emission /
wavelet basis
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Shantanu Datta, Shibayan Sarkar. A review on different pipeline fault detection methods [J]. Journal of Loss Prevention in the Process Industries, 2016(41): 97-106.
[2] Majid Ahadi, Mehrada Sharif Bakhtiar. Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals [J]. Applied Acoustics, 2010, 71(7): 634-639.
[3] Lang X, Li P, Guo Y, et al. A Multiple Leaks' Localization Method in a Pipeline Based on Change in the Sound Velocity [J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(7):5010-5017.
[4] 石光辉，齐卫雪，陈鹏，等.负压波与小波分析定位供热管道泄漏[J].振动与冲击,2021,40(14):212-218+286.
Guanghui shi, Weixue Qi, Peng Chen, et al. Experimental study of leakage location in a heating pipeline based on the negative pressure wave and wavelet analysis[J]. JOURNAL OF VIBRATION AND SHOCK,2021,40(14):212-218+286.
[5] Pan Shengshan, Xu Zhengdan, Li Dongsheng, et al. Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology[J]. Sensors, 2018, 18(11)：3628.
[6] Rui Xiao, Qunfang Hu, Jie Li. Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine [J]. Measurement, 2019 (146): 479-489.
[7] 方丽萍，张馨方，罗启姣，等. 管道泄漏信号去噪中小波基及分解尺度的选取—基于小波分析[J]. 钦州学院学报, 2017, 32(5): 18-22.
Liping Fang, Xinfang Zhang, Qijiao Luo, et al. Wavelet Basis and Decomposition Scale Selection in Pipeline Leakage Signal Denoising — Based on wavelet analysis[J]. JOURNAL OF QINZHOU UNIVERSITY, 2017, 32(5): 18-22.
[8] Wahyu Kuntjoro, Aidah Jumahat, Farrahshaida Mohd Salleh, et al. Wavelet Analysis: Mother Wavelet Selection Methods [J]. Applied Mechanics and Materials, 2013, (393): 953-958.
[9] Yueqin Li, Lei Han. The Optimal Wavelet Basis Use for Gas Pipeline Leak Monitoring and Positioning [J]. Sensors & Transducers, 2014,174 (7): 246-252.
[10] Majid Ahadi, Mehrdad Sharif Bakhtiar. Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals [J]. Applied Acoustics, 2010, (71): 634-639.
[11] Shaofeng Wang, Zhihao Chen, Jianguo Wang, et al. Continuous Leak Detection and Location through the Optimal Mother Wavelet Transform to AE Signal [J]. Journal of Pipeline Systems Engineering & Practice,2020, 11(3): 04020024.
[12] A. Moshtaghpour, J. M. Bioucas-Dias, L. Jacques. Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling With Haar Wavelet Basis [J]. IEEE Transactions on Information Theory, 2020, 66(11): 7253-7273.
[13] Anubha Gupta, Shiv Dutt Joshi, Surendra Prasad. A new method of estimating wavelet with desired features from a given signal [J]. Signal Processing, 2005(85): 147-161.
[14] M. Amabili. NON-LINEAR DYNAMICS AND STABILITY OF CIRCULAR CYLINDRICAL SHELLS CONTAINING FLOWING FLUID. Part I: STABILITY [J]. Journal of Sound and Vibration, 1999, 225(4): 655-699.
[15] A. Mostafapour, S. Davoudi. Analysis of leakage in high pressure pipe using acoustic emission method [J]. Applied Acoustics, 2013, 74: 335-342.
[16] 高晓旸，严如强. 小波变换理论及其在制造业中的应用[M]. 北京: 机械工业出版社，2019.
Robert X Gao, Ruqiang Yan. Theory and Applications for Manufacturing [M]. Springer, 2011.
[17] 马海涛. 天然气管道泄漏应力波波速估计[D]. 燕山大学，2016.
Haitao Ma. ESTIMATION OF STRESS WAVE VELOCITY IN NATURAL GAS PIPELINE LEAKAGE [D]. YANSHAN UNIVERSITY, 2016.
[18] 王宇，罗忠. 薄壁圆柱壳构件受迫振动的响应特征研究[J].振动与冲击,2015,34(07):103-108.
Yu Wang, Zhong Luo. Forced vibration response characteristics of thin cylindrical shell [J]. JOURNAL OF VIBRATION AND SHOCK, 2015,34(07):103-108.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1926 KB)
