A virtual pipe rig for testing acoustic leak detection correlators: Proof of concept

Brennan, M.J.; Lima, Fábio Kroll de; Almeida, Fabricio; Joseph, Phillip; Paschoalini, Amarildo Tabone

doi:10.1016/j.apacoust.2015.09.015

Cited by 26 publications

(7 citation statements)

References 12 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The leak noise model is based on the models of additive noise emission [28] and sound propagation along a pipe [29], respectively. According to Brennan et al [29], to simulate the leak noise signals received by the sensors, the uniformly distributed noise-like signal was processed by pipe filters with dynamic characteristics H A,B ( f, d A,B ) given by The amplitude-frequency response (AFR) of the noise-free test signals is shown in Figure 5. The scale of the graphs along the vertical axis is normalized by the maximum values.…”

Section: Leak Noise Modelmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Basic Time Domain Time-Delay Estimators for Locating Leaks in Pipelines

Faerman¹,

Avramchuk²

2020

IJNDC

View full text Add to dashboard Cite

The article is devoted to a survey of the known basic Time-Delay Estimation (TDE) methods in the application for detecting fluid leaks in pipeline transport and distribution systems. In the paper, both active and passive estimation methods are considered with respect to the presented generalized classification. However, the main attention is paid to the study of passive methods and their algorithmic implementation. Among the passive methods the Absolute Difference Function (ADF) minimizing algorithms, the Basic Cross-Correlation (BCC) algorithm, the adaptive Least Mean Square (LMS) filtering algorithm are implemented, investigated and compared using a mathematical model of a leak noise signal. All the algorithms are described and the results of the comparison are provided. The advantage of the LMS algorithm in processing the noisy signals is shown. However, its implementation requires choosing a convergence parameter. The necessity in choice of the additional parameter makes the LMS algorithm less practically applicable than the BCC algorithm. The ADF algorithm showed the lowest noise resistivity and can be considered as useful for signal detection, but not for TDE. Some conclusions regarding algorithms' use for solving the problem of locating leaks are stated. In particular, correlation-based TDE algorithms, as generalized cross-correlation and time-frequency cross-correlation, appear to be the best choice for the implementation of leak detection software.

show abstract

Section: Leak Noise Modelmentioning

confidence: 99%

“…Let us note that the featured leak noise model has its limitations. First, it is not accurate for metal pipes because it is based on the pipe filter that is developed mostly for plastic pipes [29]. Second, the model does not take into account the impact of cavitation [27] and some other effects on emitted acoustic signals.…”

Section: Leak Noise Modelmentioning

confidence: 99%

Comparative Study of Basic Time Domain Time-Delay Estimators for Locating Leaks in Pipelines

Faerman¹,

Avramchuk²

2020

IJNDC

View full text Add to dashboard Cite

show abstract

“…The acoustic emission (AE) detection method (Kim & Lee 2009;Brennan et al 2016;Gao et al 2017;Almeida et al 2018;Kassab et al 2019;Xiao et al 2020) is widely used in detection of pipe leaks due to its low cost, non-intrusiveness, and high efficiency. The classic AE pipe detection is shown in Figure 1; the acoustic or vibration sensors are closely attached to both ends of the leak pipe to capture the leak signals.…”

Section: Introductionmentioning

confidence: 99%

Leak location procedure based on the complex-valued FastICA blind deconvolution algorithm for water-filled branch pipe

et al. 2021

View full text Add to dashboard Cite

Water pipe networks have a large number of branch joints. Branch joint shunting generates vortices in the fluid, which excite the pipe wall to produce a type of branch noise. The branch noise is coupled with the leak source signal through the pipe. Here, a novel leak location protocol based on the complex-valued FastICA method (C-FastICA) is proposed to address the leak location problem under the branch noise interference. The C-FastICA, a complex-value domain blind deconvolution algorithm, effectively extended the cost function, constraint function, and iteration rules of the instantaneous linear FastICA into the complex-valued domain. The C-FastICA method was used to realize the separation of branch noise and leak source signal. The experimental results showed that the separation efficiency of the C-FastICA was higher than that of time-domain blind convolution separation (T-BCS). Furthermore, the relative location error of the C-FastICA method to the leak point was less than 14.238%, which was significantly lower than in traditional T-BCS and direct cross-correlation (DCC) technology.

show abstract

“…It is important, therefore, to have a good estimate of these properties to ensure that the current models can be validated and further developed to study the dynamic behaviour of water pipe systems for the purposes of leak detection and location. These models can also be used in the development of bench-top virtual pipe test systems, for example [27], which can be used to test and compare the performance of leak noise correlators in a variety of field conditions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the bulk and shear moduli of soil surrounding a plastic water pipe using measurements of the predominantly fluid wave in the pipe

Scussel

Brennan

Muggleton

et al. 2019

Journal of Applied Geophysics

Self Cite

View full text Add to dashboard Cite

Leak noise correlators are commonly used to detect and locate leaks in buried water pipes. They use the cross-correlation function between leak noise signals measured using hydrophones or accelerometers placed on the pipe either side of the leak. The efficacy of a correlator is dependent upon knowledge of the speed at which the leak noise propagates along the pipe as well as how much it attenuates with distance. The leak noise is carried in a predominantly fluid-borne wave in the pipe, which is heavily influenced by the pipe and soil properties. Although the pipe properties can be determined relatively easily, estimation of the soil properties surrounding the pipe is more problematic. It is desirable to have an accurate estimate of the soil properties, so that current models can be developed and used to improve understanding of leak noise propagation and hence leak detection capabilities. In this paper a novel approach to determining the bulk and shear moduli of the soil from measurements of the predominantly fluid-borne wave in a buried plastic pipe, is described. The measured data are compared with corresponding data predicted from a model, and the soil properties are determined using an optimization algorithm. The method is applied to two different sites, one in the UK, where the soil properties surrounding the pipe are representative of sandy soil, and one in Brazil, where the surrounding soil has properties that are representative of clay soil. It is found that the bulk and shear modulus can be estimated in the pipe buried in sandy soil, but in the clay soil it is only possible to estimate the shear modulus.

show abstract

A virtual pipe rig for testing acoustic leak detection correlators: Proof of concept

Cited by 26 publications

References 12 publications

Comparative Study of Basic Time Domain Time-Delay Estimators for Locating Leaks in Pipelines

Comparative Study of Basic Time Domain Time-Delay Estimators for Locating Leaks in Pipelines

Leak location procedure based on the complex-valued FastICA blind deconvolution algorithm for water-filled branch pipe

Estimation of the bulk and shear moduli of soil surrounding a plastic water pipe using measurements of the predominantly fluid wave in the pipe

Contact Info

Product

Resources

About