Wireless transmission of acoustic emission signals monitoring of leakage in underground pipes.pdf

KSCE Journal of Civil Engineering (2011) 15(5):805-812DOI 10.1007/s12205-011-0899-0

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www.springer.com/12205

Geotechnical Engineering

Wireless Transmission of Acoustic Emission Signals for Real-Time Monitoring of Leakage in Underground Pipes

Bui Van Hieu*, Seunghwan Choi**, Young Uk Kim***, Youngsuk Park****, and Taikyeong Jeong*****

Received June 9, 2009/Revised March 2, 2010/Accepted June 17, 2010

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Abstract

In this paper, we propose a system combining various methods for monitoring leakage of underground pipelines. The system usesa wireless connection for communications and uses acoustic emission effects to detect and locate leakages. The system is easilydeployed, with a flexible configuration that requires less maintenance because a wireless connection is used. Moreover, the system isaccurate, simple, and inexpensive, because it is based on acoustic emissions. Experiments determining the wireless connectivity ofthe proposed system are also presented.Keywords: wireless sensor, monitoring system, underground, acoustic emission sensor, leakage monitoring

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1. Introduction

Currently, Real-time Monitoring Systems (RMS) are importantin many areas, such as transportation, airports, and civil con-struction industries. A Wireless real-time Monitoring System(WMS) is a RMS that uses wireless communication. A WMSincludes a network of sensor nodes, which measure monitoredvalues and use wireless transmission for communication, as wellas a control center that gathers all of the values, processes them,and alerts users if there is a problem in the monitored system.Recently, WMS has been extended to monitor undergroundapplications, e.g., an environment with soil, soil-mixture andnon-soil components. Compared with monitoring systems usinga wire for communication, WMS for underground systems offersmany advantages, such as concealment, ease of deployment,timeliness of data, reliability, coverage density, and quality ofservice, etc (Akyildiz and Stuntebeck, 2006).

Pipeline systems are used as the main transportation system formany applications, such as water distribution, oil and gas trans-portation, metropolitan heat systems, etc. Leakage in these pipelinesystems can cause environmental and chemical damage. Leakagenot only wastes resources but also creates environmental pro-blems (Wu and Wang, 2006). Hence, monitoring systems forpipeline leakages are being increasingly demanded.

A leakage monitoring system involves two critical tasks: de-tecting leakage and locating a leak position. Some studies use

pressure, flow, and temperature variables to detect pipeline leaks,but these methods are only able to detect large leaks. Also, theyare difficult to deploy and have difficulty locating the leak posi-tion (Silva et al., 2005; Wu and Wang, 2006). The current pro-posed method uses acoustic emission sensors and is designed todetect an accurate point of leakage while the system is running.With only two sensors at two pipe ends, leakage can be deter-mined, and the leakage position can be resolved. Furthermore,the acoustic emission sensor is small, lightweight, and mountableon outside pipe surfaces. For those reasons, an acoustic emissiondetection method is suitable as a pipeline leakage monitoringsystem, especially when the pipeline is buried underground.

In this paper, firstly leak detection and locating methods basedon acoustic emissions are discussed first. Next, special issuesrelated to sensor nodes for underground applications and theirsolutions are presented. Then, we propose a WMS that uses anacoustic emission leak detection method to monitor an under-ground pipeline leak.Finally, experiments show the reliability ofthe proposed system.

The rest of the paper is organized as follows. In Section II,leakage sound features are discussed. Then, leakage detectionand leakage locating methods are presented in Section III andsection IV, respectively. Next, underground acoustic emissionsensor node issues are presented in Section V. In Section VI, wepresent our proposed monitoring system. Section VII shows theexperimental results, and section VIII is the conclusion.

*Master Student, Dept. of Electronic Engineering, Myongji University, Yongin 449-728, Korea (E-mail: [email protected])**Principal Researcher, Korea Electric Power Research Institute, Daejeon 305-380, Korea (E-mail: [email protected])

***Member, Professor, Dept. of Civil and Environmental Engineering, Myongji University, Yongin 449-728, Korea (E-mail: [email protected])****Member, Professor, Dept. of Civil and Environmental Engineering, Myongji University, Yongin 449-728, Korea (E-mail: [email protected])

*****Member, Assistant Professor, Dept. of Electronic Engineering, Myongji University, Yongin 449-728, Korea (Corresponding Author, E-mail:[email protected])

Bui Van Hieu, Seunghwan Choi, Young Uk Kim, Youngsuk Park, and Taikyeong Jeong

− 806 − KSCE Journal of Civil Engineering

2. Acoustic Leak Signal

When a leak happens in a pipeline, it generates acoustic sound.This acoustic signal propagates along the pipeline so we candetect the leak by detecting the acoustic signal. The followingparagraphs discuss features of an acoustic leak signal.

A leak’s frequency features, as well as its amplitude, dependon many factors, for instance, the size of leak, the type of trans-ported fluid (i.e., water, oil), and pipeline pressure. If the pipe islarge in diameter or less solid, then the leak sound contains lower-frequency components. On the contrary, if pressure is higher,then higher-frequency components dominate. The amplitude ofthe leak sound is higher if the pressure or flow speed is higher orif the leak is large, but not very large (Hunaidi and Chu, 1999;Hunaidi et al., 2004). If operational conditions of the pipeline,such as temperature, pressure, and flow do not change, then theleak sound is assumed to be a stationary signal, a signal whosefrequency components do not change over time.

Propagation of a leak signal also depends on many factors,such as type of pipeline (i.e., PVC, iron) and the pipe’s surround-ing environment. A leak signal propagates well with metal pipebut attenuates greatly with plastic or concrete pipe (Hunaidi andChu, 1999; Muggleton and Brennan, 2004). When pipe is buriedunderground, a leak signal is attenuated more than when the pipeis above ground. The signal is less attenuated in sandy soil,asphalt, and concrete but attenuated greater in clayey or grassareas (Hunaidi and Chu, 1999). Moreover, attenuation of the leaksound, while it propagates along the pipe, is not linear, i.e. atten-uation factors of different frequencies are different (Muggletonand Brennan, 2004).

While the leak sound signal propagates, noise interferes withthe leak signal. Noise generates from the flow of fluid insidepipes, wind, and construction sound. Noise can be modeled as twokinds: background or white noise and burst noise. Background/white noise is a stationary signal component, and burst noise is anon-stationary one. Stationary noise often has low amplitudes,whereas non-stationary noise often has high amplitudes andnarrow bands.

We performed experiments to check leaks above ground andunderground. Devices used in the experiment are shown in Fig.1. A metal pipe was used to transport water and simulate leaksignals. An acoustic emission sensor sensed sound signals andwas connected with an analyzer for gathering data. These datawere transmitted to a laptop over an Ethernet connection forprocessing. The metal pipe length was 6 m, and its diameter was3 cm. A leak was made on the pipe by drilling a 1.5-mm diameterhole. The acoustic emission sensor was a sensor integrated pre-amplifier. Its specifications are shown in Table 1.

Leak signals were measured by mounting a sensor on the sur-face of the metal pipe, as shown in Fig. 2(a). First, the pipe wasplaced on the ground to measure leak signals above ground.Then the pipe was buried underground at a depth of 30 cm tosimulate a leak underground. Fig. 2(b) shows the pipe setupbefore being covered by soil.

In both cases, most working conditions, such as the pipe, fluidpressure, and leak size, were identical. The only difference wasin the placement of the pipe above aground or underground.However, the measured leak signals shown in Fig. 3 are differ-ent. The above-ground leak signal had higher amplitudes andchanged more suddenly than the underground leak signal becausethe attenuation above ground was lower and noise was higherthan underground.

3. Leak Detection Procedure with Signal Trans-form

The early leak detection method based on acoustic emission isvery straightforward: a person uses a device to hear the acousticsound and determine whether there is a leak. The results depend

Fig. 1. Experiment Devices

Table 1. Acoustic Emission Sensor Specifications

Name Value

Peak sensitivity, ref V/(m/s) 120 dB

Peak sensitivity, ref V/ubar -28 dB

Frequency range 10-70 kHz

Resonant frequency, ref V/ (m/s) 25 khz

Resonant frequency, ref V/ubar 31 kHz

Directionality +/- 1.5 dB

Temperature range -35o - 70oC

Fig. 2. Outdoor Experimental Setup on Pipe Line: (a) AboveGround Pipe Setup, (b) Underground Pipe Setup


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mostly on the experience of the user. Later, many studies used acomputer to analyze the spectral features of an acoustic sound todiscover leaks (Zhan-Hui et al., 2005; Jin et al., 2008; Liu andZhao, 2008). The detection method in these studies generallyincludes two stages: extracting signal features and recognizingthe signal base on extracted features.

Extracting signal features means to extract special signalcharacteristics (Kang et al., 2009). Extracted features are used torepresent the signal and replace the original signal in the follow-ing process. Leak signal varies under different conditions, asdiscussed in the previous section. For accurate results, the systemmust extract features which reflect the signal exactly and distin-guish between leak signal and noise.

Fourier transform is a general method to extract frequencyfeatures of a signal and is used in many applications, includingsometimes as a way to extract leak signal features. Using LinearPrediction Cepstrum Coefficients (LPCC) is another way to ex-tract signal features (Changsheng et al., 2006). Recently, anotherscheme, which is promising and interesting to many researchers,is to use wavelet transform coefficients to represent signalfeatures. Compared with Fourier transform, wavelet transformcomputation is less and it reflects both the frequency and timingof the signal, whereas Fourier transform only reflects the fre-quency of the signal.

After obtaining signal features, a recognizing model uses thesefeatures to determine whether a signal is a leak signal. The model

can be a predetermine as maximum modulus (Zhan-Hui et al.,2005), Hidden Markov Machine (HMM) (Changsheng et al.,2006), or base on training values in a support vector machine(Liu and Zhao, 2008; Huali et al., 2004), or Neural network (Jiaoet al., 2006). These methods have trade-offs between complexityand accuracy. Among them, the Support vector machinebalances computation complexity and result accuracy.

With only two steps, leak detection results are very good underlaboratory conditions, but results will be less efficient in practice.These methods do not deal with noise, and noise can make theresult incorrect. Therefore, to increase accuracy, we propose anew detection procedure which has three stages, as in Fig. 4. Onemore stage is added before the Feature extracting stage. Thisstage will reject noise from the leak signal.

As mentioned in section 2, there are two kinds of noise:stationary and non-stationary noise. Stationary noise may beremoved implicitly by the recognizing model. Non-stationarynoise is an important agent affecting the recognizing results.Wavelet transform can be used effectively to remove the non-stationary noise and is presented in (Daneti, 2008).

4. Leak Locating Method

To locate a leak in the pipeline, we need input from twosensors with the condition that the leak must happen betweentwo sensors, as in Fig. 5.

From above figure, we have:

(1)

As a leak happens, it propagates and reaches sensor i andsensor j at times ti and tj, respectively. Assuming that the leaksignal propagation p, which can be predetermined easily, is con-stant along the pipe, Eq. (1) becomes:

(2)

If the difference in arrival time can be determined,then leak location can be derived by the simple Eq. (3)

With Eq. (3), leak locating requires determining ∆tij. Todetermine ∆tij, the general autocorrelation (Knapp and Carter,

L Li Lj+ 2Li Li Lj–( )–= =

L 2Li p ti tj–( )– 2Li p tij∆–= =

tij∆ ti tj–=

Fig. 3. Leak Signal in Different Locations: (a) Leak Signal AboveGround, (b) Leak Signal Underground

Fig. 4. Leak Detection Procedure

Fig. 5. Description of Leak Assumption Point



1976) is often used. This method models the receive signals attwo sensors as below:

(4)

with si(t) and sj(t) as the received signals at sensor i and sensor j,respectively. l(t) is the leak signal, α represents signal attenua-tion, and ni(t) and nj(t) represent noise. With this method, noisemust be a stationary signal, otherwise the leak locating result isincorrect. Other methods rejecting noise and determining ∆tij

concurrency are presented in Yumei et al. (2004) and Daneti(2008). When noise ni(t) and nj(t) including stationary and non-stationary signals, are removed, time lag ∆tij can be determinedeasily by a cross-correlation method as discussed in section 3.Firstly, the cross-correlation of two signals, si(t) and sj(t), arecalculated, as in Eq. (5). Then, the t value that makes Rsi,sj

maximum is determined. This value is the time lag, ∆tij.

(5)

where si* denotes the complex conjugate of the si signal.

In summary, to localize the leak position, we need data signalsfrom two sensors. First, noise including background and burstnoise is removed. Next, cross-correlation of the two sensor datais calculated. Then, combining the position which make the crosscorrelation maximize and the sample rate, we determine the timelag in arrival time of the two sensors. Finally, the leak point isdetermined by Eq. (3).

5. Underground Sensor Node

In a wireless sensor network, a sensor node collects sensorinformation and communicates wirelessly with other nodes inthe network. The most popular sensor node platform used in over-ground applications is a MICA2 board, shown in Fig. 6. AMICA2 board has an expansion connector which connects withsensors. The central part of the MICA2 is a ATMega128L micro-controller that controls the sensor gathering and communicatingprocesses. Power is provided by two AA batteries, and an addi-tional power supply connects to an external power connector,which only needs to connect with an antenna to communicatewirelessly. Its main transmission features are shown in Table 2.With a data rate of 38.4 kBaud and an outdoor range of 150 m,the MICA2 is suitable for most aboveground wireless sensornetwork applications.

Although the MICA2 is not developed for underground appli-cations, most current underground wireless sensor networks useit. Unfortunately, underground environments with different com-binations of sand, soil, clay, and moisture are unfavorable forElectromagnetic (EM) wave transmissions which are used forcommunication in MICA2 boards. Some studies model the effectof the underground environment on EM, and all of them showthat EM transmission attenuation is very high in underground

environments (Dam et al., 2005; Li et al., 2007). Experimentalresults show that the wireless communication distance cannotexceed 7 m horizontally and 0.6 m vertically for the MICA2board (Stuntebeck et al., 2006).

With these distance limitations, MICA2 is difficult to use forapplications in which the sensor nodes are buried in the ground.To increase underground communication distance, other trans-mission techniques are considered in Vasquez et al. (2004) andSojdehei et al. (2001), but more investigation is needed beforethey are practical. Hence, currently, reasonable undergroundcommunication is still EM; therefore, MICA2 is still the appro-priate selection for underground wireless communication. This isthe limitation of underground wireless sensor networks at thistime and it needs to be considered when designing undergroundmonitoring systems.

Another issue with buried sensor nodes is the power supply. Tocommunicate underground, sensor nodes must transmit signalswith higher energy than above ground; hence, they need morepower. Sensor nodes are usually battery-powered, but changingbatteries is sometimes impossible for buried sensor nodes. Thebattery has to provide enough power for the sensor node duringthe entire operating time. To provide enough energy for long-term operations, the battery size may become too big to bedeployed with a sensor node. One feasible solution is to integratea device into the sensor node that can generate electrical energyfrom other sources with the sensor node (Stordeur and Stark,1997; Ping and Yumei, 2005). This source would be an externalpower supply that provides additional power for the sensor node.

Moreover, choosing a suitable sensor for a sensor node is an

si t( ) l t( ) ni t( )+=sj t( ) αl t tij∆+( ) nj t( )+=⎩⎨⎧

RSi Sj, t( ) si* δ( ).sj t δ+( )( ) δd

∞–

∞

∫=

Fig. 6. ATMega128L Microcontroller Embedded on MICA2 Board

Table 2. MICA2 Transmission Features

Name Value

Output gain 5 dBm

Receive sensitivity -98 dBm

Signal rate 38.4 Kbaud

Outdoor range 150 m

Center frequency 300-915 MHz


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important issue. There are many types of acoustic sensors suchas piezoelectric or fiber bragging with different shapes, sizes andsensitivities. Fiber bragging sensors have many advantages, suchas accuracy, electromagnetic immunity, and harsh environmentimmunity, but they are difficult to integrate with wireless sensornodes, which require low power consumption, simplicity, andcost efficiency. With suitable features, such as small size, lightweight, and simple connection, piezoelectric sensors are themost appropriate for acoustic sensor nodes buried underground.

Piezoelectric acoustic sensors use piezoelectric effects to trans-duce acoustic sound into electricity. The frequency response ofthis sensor type can vary with frequency. Different sensors havedifferences in sense frequency range. With various expecteddetection leaks, frequency ranges are different. Consequently, thechosen sensor must have the suitable frequency response. Thewide band sensor can sense a broad range of frequencies; there-fore, it has the ability to detect many types of leak, but it makesprocessing more complex because it also includes abundantnoise.

6. Proposed Testing Platform

As mentioned in section III, sensor nodes for undergroundsystems have more issues that need to be considered. Hence, wepropose a new structure for sensor nodes of leakage monitoringsystems for underground pipelines (Fig. 7). The acoustic emissionsensor is a piezoelectric acoustic sensor which converts acousticemissions to electrical signals. A MICA2 board connects withthe sensor, gathers sensor data, and then wirelessly transfers datato the other node. Power is provided by a rechargeable battery.There is an added power generator which transforms otherenergies to electricity to provide for the operation of the sensornode. Because this power is not stable, it cannot supply powerdirectly to the sensor node. Instead, this power is stored in arechargeable battery. A battery charging control circuit controlsthe charging process and ensures that the battery is notovercharged. The battery and power generator ensures that thesensor node has enough power for its operational lifetime.

Based on analysis of the leak acoustic emission processing andwireless underground sensor node, we propose a WSN monitor-ing system for monitoring underground pipeline leak, as shownin Fig. 8.

The system includes sensor nodes that are buried underground.The sensor is mounted on a pipe surface to sense acoustic emis-

sions by converting them to electrical signals. The MICA2 boardconverts the analog electrical signal to a digital number, adds atime stamp, stores it in flash memory and then packets and trans-fers them to another node. Because underground wireless com-munication distance is relatively short, as mentioned in sectionIII, the topology of the WSN has to be designed carefully. OneMICA2 board, a wireless repeater, is placed on the ground aboveevery buried sensor node. The MICA2 board receives packetsfrom underground sensor nodes. Then, they forward these packetsto a predetermined MICA2 board, called a Data AcquiringStation (DAS). DAS receives all sensor data and then transmits itto a computer by an RS232 or LAN interface. At the computer,leak detection, location, and monitoring processes are performed.

The monitoring process at the computer involves many steps.First, noise in the sensor data is rejected. Next, the Wavelettransform is applied to extract signal features. Signal features arethe input for a Support vector machine to determine if a leakoccurred. If there is a leak, then a leak location procedure isperformed. The process is performed many times, as configuredby the user, to ensure that the result is correct. If the leak resultsare consistent, an alarm is activated. The pseudo-code of theprocess in the computer is shown in Fig. 9.

With the proposed network topology, the system is easy todeploy, configure, and maintain. One DAS can collect data fromsensor nodes in a radius of 150 m. If greater distance is needed, itcan be easily reached by placing one more MICA2 boardsbetween DAS and the MICA2 board placed above the buriedsensor node. The limitation of our proposed system is that thedepth of the monitored pipeline must be less than or equal to 0.6m because of the limits of EM transmission in undergroundenvironments.

Fig. 7. The Proposed Sensor Node Structure

Fig. 8. The Proposed Testing Platform Structure

Fig. 9. The Pseudo-code of the Monitoring Process



7. Wireless Connectivity Experiments

The wireless connectivity of our proposed testing platform wasverified by experiments. Experiments were performed outside atthe Myongji University campus.

The first experiment was performed to determine the commu-nication distance between DAS and MICA2 boards, which wereplaced above ground in our proposed system. The experimentalplan is illustrated in Fig. 10.

Two MICA2 boards were placed above ground. One boardalways sent data packets, while the other board detected thepacket. The distance between the two boards was increased untilthey couldn’t communicate or the packet wasn’t received. Inputstrength was measured, while the distance was increased (Fig.11). Input strength decreased almost linearly with distance. At adistance of 10 m, input strength was under the limit (i.e., -95dBm) of the MICA2 board. At distances greater than 10 m, thetwo boards could not communicate.

The second experiment was performed to determine the limitof the communication distance between the MICA2 board on theground and the MICA2 board buried underground. The experi-

ment plan is shown in Fig. 12. The distance between the twosensors was increased until they could not communicate. Thereceived signal strength was measured and shown in Fig. 13.Attenuation in the underground environment was much higherthan in the aboveground environment. Received signal strengthsreduced rapidly. The depth limit of a buried sensor is 30 cmbased on our real experiment.

In practice, the position of the MICA2 board above groundmay be slanted with the board buried underground. An experi-ment was performed to determine this effect. The experimentplan is shown in Fig. 14.

This experiment was similar to the second experiment. TheMICA2 board buried underground was set up at some determin-ed depth, and the MICA2 board on the ground was moved awayfrom the buried sensor position. The MICA2 board buried under-ground was placed at three depths, 10 cm, 15 cm, and 20 cm.The received signal strength was measured and shown in Fig. 15.As shown in that figure, the distance limit at a depth of 10 cm

Fig. 10. Horizontal Alignment Experiment Plan

Fig. 11. Result of Horizontal Alignment Experiment

Fig. 12. Vertical Alignment Experiment Plan

Fig. 13. Result of Vertical Alignment Experiment

Fig. 14. Skewed Alignment Experiment Plan

Fig. 15. Result of Skewed Alignment Experiment


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was 40 cm. At a depth of 15 cm, the limit was 30 cm, and, at adepth of 20 cm, it was 22 cm.

Applying the Pythagorean Theorem in the experiment illustrat-ed in Fig. 14, we have:

(6)

where w is the direct underground connection between the twosensor boards. We can see it as the underground transmissiondistance. However, this distance decreases if the sensor is burieddeeper underground. Maximum values of this distance are 65cm, 76 cm, 38 cm and 28 cm if the buried sensor board is placedat depths of 10 cm, 15 cm, and 20 cm, respectively.

8. Conclusions

In this paper, we discussed techniques using acoustic emissioneffects to recognize a leak and locate its position. We proposedremoving noise before processing leak signals. We also analyzedissues of underground wireless sensor nodes that need to beconsidered and proposed a structure for underground sensornodes. Based on these analyses, we proposed a monitoring sys-tem that uses acoustic emission effects to monitor undergroundpipeline leakages. Our proposed system uses wireless communi-cation so that it can be easily deployed, configured, and main-tained. Experiments showed that the communication limits ofour proposed system were 10 m horizontally and 30 cm verti-cally.

Acknowledgements

This work is supported by National Science Foundation (NRF)grant funded by the Korea gov. (MEST) (No.20090069991)andalso supported by the 2010 research fund of Myongji Universityin Korea. This work is supported by the Korea gov. Ministry ofKnowledge and Economics (MKE) under the grant No. I-2010-1-012 of the Electric Power Industry Tech. Evaluation andPlanning Center (ETEP). The circuit was designed at IC DesignCenter.

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Wireless transmission of acoustic emission signals monitoring of leakage in underground pipes.pdf