The pipeline leakage detection and location method based on negative pressure wave (NPW) characteristics are widely used in existing pipe network systems. The accuracy of leakage localization significantly relies on precisely picking up the arrival time of the NPW. A novel method based on Bayesian detection theory is proposed to determine both the arrival and the departure times of the NPW. The use of NPWs as features was theoretically derived based on the transient fluid dynamics of the pipeline, and then the nonstationary NPW was transformed into a piecewise stationary Gaussian process by differencing the pressure time series data. A Bayesian optimal detector was constructed to identify the multiple transition points (corresponding to the arrival and departure times) in the differentiated pressure data to identify the NPW. A series of pipeline leakage tests were carried out to verify the effectiveness of the proposed method. It is demonstrated that the combination of time differencing nonstationary pressure data and employing the Bayesian detector can precisely capture the arrival and departure times of NPWs, enabling more accurate leakage localization in the pipeline.
NegmAMaXAggidisG.Review of leakage detection in water distribution networks. IOP Conf Ser Earth Environ Sci2023; 1136(1): 12052.
2.
ColomboAFLeePKarneyBW.A selective literature review of transient-based leak detection methods. J Hydroenviron Res2009; 2(4): 212–227.
3.
GeCWangGYeH.Analysis of the smallest detectable leakage flow rate of negative pressure wave-based leak detection systems for liquid pipelines. Comput Chem Eng2008; 32: 1669–1680.
4.
LiJZhengQQianZ, et al. A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Saf Environ Prot2019; 123: 309–316.
5.
ChenQShenGDJiangJC, et al. Effect of rubber washers on leak location for assembled pressurized liquid pipeline based on negative pressure wave method. Process Saf Environ Prot2018; 119: 181–190.
6.
LiW.Pipeline leak detection and localization based on advanced signal processing and negative pressure wave analysis. Columbus, OH: American Society of Mechanical Engineers (ASME), 2022.
7.
LiYSunLJinS, et al. Long range hot oil pipeline leak detection and location technique based on negative pressure wave. Proc Int Pipel Conf2006; 2: 963–968.
8.
HanYFengXToddMD.A novel methodology for quantitative identification of pipeline leakage and negative pressure wave velocity. Struct Health Monit2023; 22(4): 2267–2279.
9.
BaiFGagarDFooteP, et al. Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals. Mech Syst Signal Process2017; 84: 717–730.
10.
KampelopoulosDPapastavrouGNKousiopoulosGP, et al. Defining thresholds for leak detection parameters through statistical analysis of the noise in water/gas pipelines. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, Dubrovnik, Croatia, 25–28 May 2020, pp. 1–6.
11.
WangGYFangRSunK, et al. A STA/LTA based pipeline leak detection method. Trans Beijing Inst Technol2020; 40(7): 760–764.
12.
LiYNiZTianYA.Arrival-time picking method based on approximate negentropy for micro-seismic data. J Appl Geophys2018; 152: 100–109.
13.
AlmeidaFCLBrennanMJJosephPF, et al. Towards an in-situ measurement of wave velocity in buried plastic water distribution pipes for the purposes of leak location. J Sound Vib2015; 359: 40–55.
14.
DongLChaiSZhangB.Leak detection and localization of gas pipeline system based on wavelet analysis. In 2nd International Conference on Intelligent Control and Information Processing, Harbin, China, 25–28 July 2011, pp. 478–483. IEEE.
15.
PomponiEVinogradovADanyukA.Wavelet based approach to signal activity detection and phase picking: application to acoustic emission. Signal Process2015; 115: 110–119.
16.
Piñal-MoctezumaFDelgado-PrietoMRomeral-MartínezL.An acoustic emission activity detection method based on short-term waveform features: application to metallic components under uniaxial tensile test. Mech Syst Signal Process2020; 142: 106753.
17.
KurzJHGrosseCUReinhardtH.Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. Ultrasonics2005; 43(7): 538–546.
18.
LiuMYangJCaoY, et al. A new method for arrival time determination of impact signal based on HHT and AIC. Mech Syst Signal Process2017; 86: 177–187.
19.
ButterfieldJDMeruaneVCollinsRP, et al. Prediction of leak flow rate in plastic water distribution pipes using vibro-acoustic measurements. Struct Health Monit2018; 17(4): 959–970.
20.
MounceSRPedrazaCJacksonT, et al. Cloud based machine learning approaches for leakage assessment and management in smart water networks. Procedia Eng2015; 119: 43–52.
21.
RashidSAkramUKhanSA.WML: Wireless sensor network based machine learning for leakage detection and size estimation. Procedia Comput Sci2015; 63: 171–176.
22.
SrirangarajanSAllenMPreisA, et al. Wavelet-based burst event detection and localization in water distribution systems. J Signal Process Syst2013; 72(1): 1–16.
23.
BianchiDMayrhoferEGröschlM, et al. Wavelet packet transform for detection of single events in acoustic emission signals. Mech Syst Signal Process2015; 64–65: 441–451.
24.
SedlakPHiroseYEnokiM.Acoustic emission localization in thin multi-layer plates using first-arrival determination. Mech Syst Signal Process2013; 36(2): 636–649.
25.
KitagawaGAkaikeH.A procedure for the modeling of non-stationary time series. Ann Inst Stat Math1978; 30: 351–363.
26.
WylieEBStreeterVLSuoL.Fluid transients in systems. Hoboken, NJ: Prentice-Hall, 1993.
27.
BergantATijsselingAVitkovskyJ, et al. Parameters affecting water-hammer wave attenuation, shape and timing—part 1: mathematical tools. J Hydraul Res2010; 46(3): 28–48.
28.
ZhangQTianZLuS, et al. Numerical simulation of water hammer in pipeline system using efficient wave tracking method. Water Resour Manag2023; 37, 3053–3068.
29.
SusovanPPrashanthHBryanK.An overview of the numerical approaches to water hammer modelling: the ongoing quest for practical and accurate numerical approaches. Water2021; 13: 1597.
30.
SBokaiKaiWYanfengL, et al. Oil pipeline leak detection and localization based on characteristic line method and wavelet analysis method. Adv Fine Petrochem2018; 19(4): 27–33.
31.
MadarshahianRZiehlPCaicedoJM.Acoustic emission Bayesian source location: onset time challenge. Mech Syst Signal Process2019; 123: 483–495.
32.
AlawadhiATartakovskyDM.Bayesian update and method of distributions: application to leak detection in transmission mains. Water Resour Res2020; 56(2): e2019WR025879.
33.
MadarshahianRZiehlPToddMD.Bayesian estimation of acoustic emission arrival times for source localization. Model Validation Uncertainty Quantif2020; 3: 127–133.
34.
ZhangFNgHKTShiY.Bayesian duality and risk analysis on the statistical manifold of exponential family with censored data. J Comput Appl Math2018; 342: 534–549.
35.
MingZZhangYTaoJ, et al. A method to determine the hyper parameters of the new Dirichlet prior distribution. J Astron2008; 29(6): 2062–2067.
