an evaluation on the multi-agent system based structural health monitoring for large scale...

Expert Systems with Applications 36 (2009) 4900–4914

Contents lists available at ScienceDirect

Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

An evaluation on the multi-agent system based structural health monitoringfor large scale structures

Xia Zhao *, Shenfang Yuan, Hengbao Zhou, Hongbing Sun, Lei QiuThe Aeronautical Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics 173#, 29# Yu Dao Street, Nanjing 210016,People’s Republic of China

a r t i c l e i n f o

Keywords:Multi-agent technology
Structural health monitoringWireless sensorDamage recognitionLarge structures
0957-4174/$ - see front matter � 2008 Published bydoi:10.1016/j.eswa.2008.05.056

* Corresponding author. Tel.: +86 25 84893484; faxE-mail address: [email protected] (X. Zhao

a b s t r a c t

Different kinds of sensors have to be adopted to have a reliable monitoring of large scale engineeringstructures. Multi-agent system (MAS) based structural health monitoring (SHM) technology is researchedto deal with the diverse, heterogeneous and distributed information. This paper introduces an evaluationon a multi-agent system based structural health monitoring to validate the efficiency of the multi-agenttechnology. Through the cooperation of different agents and different subsystems, the whole system canfuse three kinds of sensor information including strain gauge sensor, fiber optic sensor and piezoelectricsensor, can automatically choose sensing object, can self-organize the sensor network and discard uselesssensor data, and can recognize three typical kinds of structure states which may indicate structural dam-ages including impact load, joint failure, strain distribution change on every substructure and the edgearea between two adjacent substructures. In the evaluation work besides adopting conventional monitor-ing instruments, the strain signal of the strain gauge is also demonstrated to be monitored by smart wire-less sensor with on-board microprocessor developed in the lab. In this paper the evaluated experimentalaluminium plate and the sensor distribution are firstly introduced. The corresponding agents are definedbased of the designed multi-agent system frame presented by the authors. The monitoring strategies inthe validation work including monitoring principle and monitoring equipments for three typical kinds ofstructure states are proposed. After the process of developing the impact load diagnostic agent as thebasis work of the whole system is introduced in detail, the implementation of the whole multi-agent sys-tem based structural health monitoring for the plate is presented. This paper shows the efficiency of themulti-agent technology for the development of the SHM system on the large practical structures.

� 2008 Published by Elsevier Ltd.

1. Introduction

The important large infrastructures include aging fleet of air-crafts, high-rise buildings, long-span bridges, ship structures,highways, cooling towers at power stations, mountains andsub-sea tunnels and other structures. The performance of thein-service structures can be affected by degradation resultingfrom exposure to severe environment conditions or damagesresulting from external conditions, such as impact, loading, oper-ator abuse or neglect. It is desirable to detect any signs of dam-age as early as possible and allow appropriate intervention to betaken since the failure of the important large structure maycause tremendous disaster. Much attention has been focusedon the research of structural health monitoring (SHM), since itprovides a reliable, efficient, and economical approach to in-crease the safety and reduce the maintenance costs of engineer-ing structures (Boller & Buderath, 2007; Sohn et al., 2003; Yuan,

Elsevier Ltd.

: +86 25 84892294.).

Xu, & peng, 2004). Using the SHM technology on the large com-plicated practical structures, the challenge is how to coordinateand manage the large dense sensor network; how to fuse theinformation from different kinds of sensors to take advantageof different estimation methods to make a reliable estimationof the whole structure, since the sensor information at differentsites of the structure is immense, diverse, distributed and heter-ogeneous. Multi-agent technology in artificial intelligence (AI)area can be adopted to solve the problems. Multi-agent system(MAS) over the past few years has come to be perceived as cru-cial technology not only for effectively exploiting the increasingavailability of diverse, heterogeneous and distributed on-lineinformation sources, but also as a framework for building large,complex and robust distributed information processing systemswhich exploit the efficiencies of organized behavior (Jennings,1998). It is helpful and useful to design MAS for SHM area. Muchresearch shows that MAS do not provide systems integrationcapabilities only, the technology permits the development ofmore intelligent diagnostic and monitoring functions (Van DykeParunak, 2000; Wang, Wang, & Xu, 2005).

mailto:[email protected]

http://www.sciencedirect.com/science/journal/09574174

http://www.elsevier.com/locate/eswa

X. Zhao et al. / Expert Systems with Applications 36 (2009) 4900–4914 4901

A design method for MAS based SHM system has been pre-sented by the authors (Xia Zhao, Shenfang Yuan, & Zhenhua Yu,

in press). Through several key aspects design such as ontology de-sign, distributed database realization, facilitator design, the fourtypical characteristics of agents including autonomy, social abil-ity, reactivity, and pro-activeness have been exhibited. A casestudy is presented to evaluate the efficiency of the proposedMAS based SHM system design strategy and the advantages ofthe MAS based SHM system. This paper introduces in detail thevalidation work of the case study on a large practical aluminumplate. The evaluation work of the multi-agent system basedstructural health system is performed to verify that the overallsystem can fuse three kinds of sensor information includingstrain gauge, fiber optic sensor and piezoelectric sensor, can inte-grate different monitoring equipments, can automatically choosesensing object, self-organize the sensor network, discard uselesssensor data, and recognize three typical kinds of structure stateswhich may indicate structural damages including impact load,joint failure, strain distribution change on every substructure ofthe plate and the edge area between two adjacent substructures.The advantages comparing to the conventional system are shownthrough the result of implementation at the end of the article.The evaluation work in this paper is expected to prove that mul-ti-agent technology is efficient for large structural healthmonitoring.

2. The development of the MAS based SHM system

The MAS based SHM architecture the author presented isshown in Fig. 1. The design of this architecture is according tothe following consideration. Considering a typical SHM system,the sensors are embedded in or bonded to the structure to sensethe structural parameters. Appropriate signal or information pro-cessing methods are adopted to analyze and extract the featuressensitive to the structural damage extracted from the sensed data.The local health status of the structure can be deduced using cor-responding damage evaluation methods. Three kinds of agents aredefined according to the three different functions. They are sens-ing agents (SA), signal processing agents (SPA), damage evalua-tion agents (DEA) (Yuan, Lai, Zhao, Xu, & Zhang, 2006; Zhao etal., in press). A large scale structure can be divided into some sub-systems to be analyzed. Thus an information layer can be added

User Interface Agent

Central Information Fusion Agent

DEA

SPA SA

Facilitator 1

SIMA

D

SPA

SA

Facilitator 2

Large structure

Subsystem1 Subsys

Fig. 1. Developed

to take charge of fusing the damage information from local sub-systems and provide the whole information to the user. The infor-mation layer is realized by the central coordination agent (CCR),the central information fusion agent (CIFA) and the user interfaceagent (UIA). CCR is in responsible for the coordination of subsys-tems, such as conflict solving, measurement time synchroniza-tion, decision making on recourse share methods andnegotiation methods etc. CIFA is in charge of fusing the damageinformation from different subsystems to give a global estimationof the whole structure. UIA provides information to the user andaccept the user’s instruction. In every subsystem, sharing infor-mation management agents (SIMA) is designed. It mainly includestwo functions: information service for local subsystem and exter-nal information sharing. The whole structure’s damage evaluationwork can be realized through subsystem’s cooperation. In everysubsystem, the Facilitator provides ‘‘yellow pages” services toevery kind of agent.

3. Multi-agent system based structural health monitoringevaluation

In order to validate the efficiency of the MAS based SHM sys-tem, a plate structure as the typical engineering structure isadopted. Three typical structure states which may indicate struc-tural damages are researched such as the impact, joint failureand strain distribution change.

3.1. System setup

The structure and the sensor arrangement are shown in Fig. 2.The setup of the practical system is shown in Fig. 3. The structureis a 2 m � 1.2 m aviation hard aluminium (the type is LY12) platewith 2.5 mm thick, fastened to a steel frame by 64 bolts. The boltsare deployed around the frame with a distance of 100 mm. Exceptfor the edge area with 100 mm � 110 mm area to arrange the bolts,the whole structure is divided averagely into eight substructureswith the dimension of 450 mm � 490 mm. Each substructure is di-vided into nine sub-areas, labeled from 1 to 9 shown in Fig. 2. Thedimension of every sub-area is 150 mm � 140 mm. Three kinds ofsensors are incorporated in the structure, piezoelectric ceramic(PZT) sensor, Fiber Bragg Grating (FBG) and strain gauge. Fifteenpiezoelectric ceramic sensors with 10 mm diameter are fixed on

EA

SIMA

DEA

SPA

SA

Facilitator N

SIMA

tem 2 Subsystem N

Central Coordination Agent

MAS for SHM.

Fig. 2. System setup.

Fig. 3. The picture of the aluminium plate and the sensor distribution.

4902 X. Zhao et al. / Expert Systems with Applications 36 (2009) 4900–4914

the corners of each substructure. Except substructure 3, four straingauges are imbedded into the area 1, area 3, area 7 and 9. In sub-structure 3, four FBG sensors are incorporated into the area 1, area3, area 7 and area 9.

Taking advantages of the multi-agent technology, this struc-tural health monitoring system should demonstrate followingfunctions: the system can automatically choose sensing object,choose suitable signal processing method and damage evaluationmethod, self-organize the sensor network and discard useless sen-sor data to localize the joint failure around the frame, the impactload position and the static load on the structure which causethe strain distribution change in the structure. To have a bettershow of the advantages of the MAS based SHM, the monitoringcase is also given to monitor the static load happened in the edgearea between two adjacent substructures by fusing the FBG andstrain gauge signal.

3.2. Multi-agent system development

According to the MAS based SHM structure, the agents de-signed in the system are as following:

3.2.1. Data monitoring agents
(1) Strainsensing agent (Strain SA): Monitoring the concentrated
load position applied to the aluminium plate through mea-suring the static strain variation. Two kinds of strain SAincluding Strain Gauge SA and Fiber optic SA are adoptedin the system to monitor the static strain signal.

(2) PZTsensing agent (PZT SA): Monitoring the joint failure usingthe active monitoring method and localizing the impactload based on the passive monitoring method (Boller &Buderath, 2007).

3.2.2. Data interpretation agents
(1) Static load signal processing agent (SLSPA): Implemented by
software including moving average for the signal from thefiber optic sensor and the strain gauge sensor.

(2) Bolt signal processing agent (BSPA): Implemented by soft-ware including extracting the peak value of the responsemonitored by the PZT SAs.

(3) Impact load signal processing agent (ILSPA): It calculates thetime delay between two Acoustic Emission (AE) signalscaused by the impact.

3.2.3. Damage diagnostic agents
(1) Static load estimation agent (SLEA): Implemented by the pat-
tern recognition software based on Euclidean distance algo-rithm realized in the computer system to recognize thepattern output of the Fiber optic SA network or Strain GaugeSA network when load is applied at different positions onthe plate.

(2) Bolt estimation agent (BEA): Implemented by Euclidean soft-ware in the computer system to distinguish which bolt isloosening from the pattern output by the PZT SA network.


(3) Impact load estimation agent (ILEA): Implemented by soft-ware in the computer to calculate the impact position onthe plate.

3.2.4. Information layer agents
(1) Central information fusion agent (CIFA): Implemented by soft-
ware in the computer to fuse the different information fromthe different local subsystems to obtain the most reliableand precise conclusion.

(2) Central coordination agent (CCR): Implemented by the soft-ware in the computer. It coordinates the whole monitoringprocess. It decides when to communicate with SLSPA andSLEA to start the static load position process monitoring,when to self-organize the sensor network, when to beginimpact load localization process and when to deal with thejoint bolt loosening.

(3) User interface agent (UIA): Implemented by the software inthe computer and the monitor of the computer system toaccept commands from the user and show the monitoringresults to the user.

4. Monitoring strategy

4.1. Static load monitoring strategy

The static load localization is based on the strain distributionvariation monitored by the strain SA network. A loading equipmentis designed to apply the load shown in Fig. 4. In this paper, the loadis used to change the strain distribution in the plate. 70N is chosen

Fig. 4. The loading equipment.

YZ-22

Switch box

The

sig

nal o

f str

ain

gaug

e an

d co

mpe

nsat

ion

gaug

e i

nput

012

98

99

……

… Am

plifi

er

strain indicator

Fig. 5. The schematic diagram for mo

in the demonstration. The four outputs of the FBG or strain gaugeSA network form a pattern to represent the strain distribution ofthe substructure. When the concentrated load is applied on thestructure or the applied position changes, the strain distributionchanges correspondingly and the output mode of the strain SAchanges too. The SLEA classifies the different pattern to decidethe different load position. The pattern recognition method thatthe SLEA adopted is the minimum-distance classification (Yuanet al., 2006). The distance between two patterns is calculated usingthe Euclidean distance, shown in Eq. (1)

dðx; yÞ ¼Xn

i¼1

bxi � yic2

" #1=2

; ð1Þ

where x, y indicate two different patterns, xi refers to the strainmonitored by each SA and yi refers to the reference strain storedin the data base related to different load position.

Two kinds of strain SA are adopted in the system includingStrain Gauge SA and Fiber optic SA. The strain gauge is the mostusual sensor to monitor the strain, which has cheap price and sta-ble performance. In the system, the signal of strain gauge is sam-pled by the equipment of YJ-33 static resistance strain indicatormade in The Shanghai Automation Instrumentation Company.The YJ-33 indicator is an intelligent stain gauge instrumentequipped with W78E516 microprocessor chip. Combined withYZ-22 Switch Box and strain indicator shown in Fig. 5 the strainsignal can be monitored automatically. The strain indicator is con-nected with the computer through RS232.

In the evaluation, the strain signal of strain gauge is also dem-onstrated to be monitored by wireless sensor developed in thelab as shown in Fig. 6. These small size, low-cost wireless sensorswhich can integrate sensing, processing and communication capa-bilities together and can form an autonomous entity (Marsh, Ty-nan, & O’Kane, 2004). By adopting wireless smart sensor withembedded microprocessors, portion of signal processing and com-putation can be done locally and simultaneously. The amount ofinformation needs to be transmitted over the network is reducedand the system speed is improved. In addition, with the wirelesscommunication links instead of the wires, the system weight andcomplexity can be greatly dropped. As shown in Fig. 7, the straingauge is connected to the wireless sensor node through the bridgecircuit and condition circuit. The wireless transmission protocol isbased on Zigbee protocol (Zig Bee Alliance, 2006).

The Fiber Bragg Grating (FBG) sensors are used in the evaluationbecause of their inherent advantages including their ability to beresistant to electromagnetic interference, light weight, multiplex-ing and few output wires. In the system, the FBG signal is

LCD Display

Keyboard

A/D

Con

vert

er

Sin

gle

chip

M

icro

pros

sor

Computer

RS232

nitoring the strain gauge signal.

Fig. 6. Wireless sensor nodes developed in the lab.


monitored by the Micro Optics si425 Swept Laser Interrogator fromMicron Optics Inc. The interrogator provides rapid, accurate mea-surements for hundreds of optic fiber sensors. As shown in Fig. 8in substructure 3 of the aluminium plate, four series FBGs are con-nected with the demodulation equipment.

4.2. Bolt loosening monitoring strategy

The bolt loosening monitoring is based on the structural vibra-tion response. PZT sensors can act both as the actuator or the sen-sor. Based on this principle, cycle active monitoring method isdesigned in the paper. Except PZT SA 7, 8, 9, the PZT SAs aroundthe boundary act as actuator in turn. Each time the signals of theadjacent PZT SAs (left and right) are sampled. For example, asshown in Fig. 9, while PZT SA 1 acts as the actuator, the signalsof PZT SA 2 and 6 as the sensors are sampled. The two sensor sig-

Fig. 7. The connection of the strain gauge, bridge circu

FiberFBG2, λ=1547nm

FBG1 λ=1552nm

FBG4, λ=1557nm FBG3, λ=1537nm Micro Optic

Fig. 8. The connection of four series FB

nals can be respectively denoted as signal 1–2 (actuator–sensor)and signal 1–6. In turn, while PZT SA 2 acts as the actuator, the sig-nal 2–1 and the signal 2–3 are sampled.

The PZT actuator is adopted here to give a sine wave excitationto the structure at 100 kHz. The reason to choose 100 kHz excita-tion here is because lots of experiments show that the vibrationresponse of the structure under this excitation is sensitive tothe bolt loosening. As shown in Fig. 10, the sensor signal variesbefore the bolt loosening and after the bolt loosening. Table 1shows after the same bolt looses, the actuator–sensor signal hasthe satisfying stability. Except the PZT SA 7, 8, 9, the other PZTSAs close to the boundary are organized to form the sensor net-work. Using cycle active monitoring method, BSPA extracts thepeak value of the actuator–sensor to form the signature mode.When there is one bolt loosening in the structure, the vibrationresponse changes. Using the Euclidean distance shown in Eq.(1), the mode can be distinguished. The cycle actuator–sensor sig-nals are sampled by the integrated PZT scanning system devel-oped in the lab.

The developed integrated PZT scanning system can interrogatelarge numbers of actuator–sensor channels automatically and effi-ciently. As shown in Fig. 11, the system integrates card form instru-ments including a waveform generation card, an analogy inputcard, a gain programmable charge amplifier card and a digital I/Ocard. These are all based on PCI-bus. A wideband power amplifierfrom The Accelent Company and two relay boards are also adoptedto realize the integration work. The scanning work is achievedthrough controlling the digital I/O card to control the relay boards.The gain programmable charge amplifier card is developed by thelab to meet the integration requirements.

it, condition circuit and the wireless sensor node.

s si425 Swept Laser Interrogator

Ethernet

G, Interrogator and the computer.

Fig. 9. Cycle active monitoring.


4.3. Impact load localization strategy

Impact load localization monitoring process is based on theimpact caused Acoustic Emission (AE) technology (Tao, 1997).AE may be defined as a transient elastic wave generated by therapid release of energy. Impact on the structure causes the AEpropagated in the structure. The AE signals are detected and con-verted to voltage signals by the PZT SA. By comparing the differ-ent time-of-flight of the acoustic signals monitored by each SA,the impact position can be localized with four point circularitylocalization algorithm (Tao, 1997). In every substructure, fourPZT SAs are mounted with a rectangular as shown in Fig. 12.

Table 1The stability of the actuator–sensor signals (max amplitude: v)

Times 1 2 3 4 5

1–2 1.596 1.622 1.589 1.593 1.5972–3 0.949 0.974 0.953 0.942 0.956

0 20 40 60 80 100-1

-0.5

0

0.5

1

volta

ge (

V)

0 20 40 60 80 100-2

-1

0

1

2

volta

ge (

V)

Before bolt loose

After bolt loose

Fig. 10. The amplitude of the signal 3–4 va

The time delay calculation process is implemented in ILSPA. Sup-pose the time when PZT SA1, PZT SA2, PZT SA3 and PZT SA4 re-ceive the AE signal are t1, t1 þ Dt1, t1 þ Dt2 and t1 þ Dt3

respectively. The propagation velocity is supposed to be v. Thelocalization coordinate of the impact load can be calculated usingEq. (2)

x ¼ v2Dt1½Dt2ðDt2 � Dt1Þ � Dt3ðDt3 � Dt1Þ�4aðDt3 � Dt2 þ Dt1Þ

;

y ¼ v2Dt3½Dt2ðDt2 � Dt3Þ � Dt1ðDt1 � Dt3Þ�4bðDt3 � Dt2 þ Dt1Þ

: ð2Þ

In the experiment, the AE signal from PZT SA is sampled by theDiSP system made in Physical Acoustic Corporation (PAC). The DiSPis based on PCI-DSP boards. The key features of every PCI-DSPboard include four digital AE channels, 16 bit, 10 MHz A/D con-verter, four High Pass, four Low Pass filter selection for each chan-nel, totally under software control. In the experiment, the bandpass frequency is set from 10 to 1200 kHz. The source of AE signalis simulated by the pencil lead break as shown in Fig. 13.

5. The implementation of the multi-agent system

The evaluated multi-agent system can automatically realize thethree health monitoring functions including the static load localiza-tion, bolt loosening monitoring and impact load localization. Accord-ing to the three different functions realized, the evaluated multi-agent system is designed shown in Fig. 14. For each diagnostic agent,the cooperation of the individual agents is shown in Figs. 15(a),

6 7 8 9 10

1.584 1.585 1.611 1.604 1.5850.946 0.945 0.944 0.956 0.948

120 140 160 180 200 t im e (us )

120 140 160 180 200 t im e (us )

0.58V

1.01V

ning

ning

ries before and after the bolt loosing.


15(b), 15(c). The implementation of the three diagnostic agents is thebasis for the realization of the whole multi-agent system.

5.1. Developing the impact load diagnostic agent

The diagnostic agent is designed based on the BDI (Belief–De-sire–Intention) model (Wooldridge & Jennings, 1995). The struc-

Fig. 11. The photo of the PZT scanni

Fig. 12. The schematic diagram of four point circularity localization algorithm.

Fig. 13. The sampling sy

ture of the diagnostic agent is shown in Fig. 16. The diagnosticagent has sensing data input, database, knowledge base, inferenceengine, user interface and the communication interface. Whenthere is an impact load occurs, the agent stores the AE signal indatabase and the diagnostic request is submitted to the diagnosticagent. Inference engine is the engine that determines the basicrules and strategy of using the knowledge for diagnostic goal

ng system and its components.

stem for AE signals.

Subsystem 1 Subsystem 2 Subsystem 8

Large structure

Impact load Diagnostic agent

Bolt loosening Diagnostic agent

Central information fusion agent Central coordination agent

User Interface Agent

Static load Diagnostic agent

Fig. 14. The multi-agent system architecture.

Impact load

Diagnostic agent Facilitator

ILSPA

PZT SA

ILEA

Fig. 15(a). Impact load diagnostic agent.

Bolt loosening

Diagnostic agent Facilitator

BSPA

BEA

PZT SA

Fig. 15(b). Bolt loosening diagnostic agent.

Facilitator

SLSPA

Strain SA

SLEA

Static load

Diagnostic agent

Fig. 15(c). Static load diagnostic agent.

0 1 2 3-4

-2

0

2

4AE signal 1

0.5

Am

plitu

de (

v)


achievement. Under the effect of inference engine, the agent que-ries the knowledge base and chooses the suitable ILSPA and ILEAto localize the position of the acoustic emission source. To realizethe diagnostic agent, the development of the knowledge baseand the efficient inference engine is the key factor.

5.1.1. The construction of the knowledge base for the impact loaddiagnostic agent

In the evaluation, the impact load diagnostic agent can not onlymeet the diagnostic demand of the aluminium plate in this paper,but also it can be suitable for other materials. Four different timedelay methods (four ILSPAs) including three common time delaymethods and advanced wavelet analysis method are experimentedand compared on three different material plates which are alumin-ium plate, glass fiber-epoxy composite plate and carbon fiber plate.The acoustic emission source between two PZT SAs and within the

Object Sensing data input

Database

Inference engine

Knowledge base

User interface Diagnostic request Communication interface

Fig. 16. The structure of diagnostic agent.

rectangle area formed by four PZT SAs can be localized. Thus twoILEAs are researched in the paper. The four ILSPAs are introducedin detail according to a typical set of AE signals sampled fromthe evaluation experiment.

(1) ILSPA1 (Threshold value method)Using this method, if the amplitude of the signal the PZT SAreceived reaches a threshold value, the AE signal is supposedto arrive at the PZT SA. As shown in Fig. 17, if the thresholdvalue is set to 0.5v, the time delay of the two AE signals canbe obtained. ILSPA1 is the easiest and obvious time delay deci-sion method, but the localization accuracy is relevant to thethreshold value. This method is suitable for the occasion whenthe Signal to Noise Ratio of the signal is high.(2) ILSPA2 (The maximum energy method)As shown Fig. 18, after AE signal smoothing and filtered, themaximum energy interval is picked up with the same widthwindow. The time delay of two AE signals is calculated by theinitial time difference of two windows. Compared with themethod of simply picking up the maximum amplitude, thismethod has the advantage of eliminating the disturbance ofthe burst noise. Using this method, the suitable window shouldbe carefully chosen. Its width should be picked up according tothe signal. In this paper the signal is processed after five pointmoving average. The window is set to 50 point sampling width.(3) ILSPA3 (Cross-correlation coefficient method)Cross-correlation is the similarity of two AE signals (Yu, Yang, &Shu, 2006). One of the signals has the time delay compared withthe other signal. The maximum of the absolute value of thecross-correlation coefficient for the two signals is the time delayof arrivals as shown in Fig. 19. Since the energies of two AE sig-nals are different, the AE signals need to be normalized beforethe cross-coefficient processing.(4) ILSPA4 (Time delay method based on the module analysisand wavelet transform)It is believed that the acoustic emission signal propagating inthe structure has the characteristics of multi-mode and disper-sion. The acoustic emission source location should be localizedusing the arrival time of the different AE signals obtained fromboth the same mode and the same frequency (Jiao, He, & Wu,2005). The wavelet transform is used to resolve the problem.By utilizing the time-frequency data of the wavelet, the arrivaltime of the AE signals to different sensors at the same frequency

x 10-4Time (s)

0 1 2 3

x 10-4

-2

-1

0

1

2

Time (s)

AE signal 2

tΔ

0.5

Am

plitu

de (

v)

Fig. 17. The schematic diagram for ILSPA1.

0 1 2 3

x 10-4

-4

-2

0

2

4

Time (s)

AE signal 1 after data smoothing

0 1 2 3

x 10-4

-2

-1

0

1

2

Time (s)

AE signal 2 after data smoothing

Max energy interval,50

Max energy interval,50

tΔ

Am

plitu

de (

v)A

mpl

itude

(v)


Time (s) 0 1 2 3 4 5 6

x 10-4

-20

0

20

40Autocorrelation of AE signal 1

0 1 2 3 4 5 6

x 10-4

-40

-20

0

20Cross-correlation of AE signal 1 and AE signal 2

tΔ

Coe

ff

Coe

ff


0 0.5 1 1.5 2 2.5

x 10-4

0.2

0.4

0.6

0.8

1

Time (S)

0 0.5 1 1.5 2 2.5

x 10-4

0.5

1

1.5

Time (S)

AE signal 1 after Gabor wavelet

Center frequency is180KHz

Center frequency is 180KHz

AE signal 2 after Gabor wavelet

tΔ

Coe

ff m

odul

e C

oeff

mod

ule


PZT1 PZT2 Ax L-x

t t+ Δ t

Fig. 21. Two point line localization.


is easily obtained. When the pencil lead is broken vertical to theplate, the main mode of guide propagation is A0 mode. In theevaluation of this paper, it is found that the frequency rangeof AE signals caused by the pencil lead break on the plate isfrom 100 kHz to 400 kHz. 180 kHz is chosen to be the centerfrequency of the wavelet transform. Gabor wavelet with thehigh time-frequency resolution is chosen as the wavelet inthe analysis. Fig. 20 is a set of AE signals after Gabor wavelettransform when the center frequency is 180 kHz. The timedelay of the two AE signals is calculated by the time differenceof the two maximum amplitudes. According to the time delaywhen A0 arrives at two sensors with the same frequency andthe group of velocity of the A0 mode at 180 kHz, the AE sourcelocalization is realized.(5) ILEA1 (Two points localization algorithm)As shown in Fig. 21 the distance between two PZT SAs is L. Sup-pose the time when PZT SA1 receives the AE signal ist, the timewhen PZT SA2 receives the AE signal is t + Dt. The time delaycalculation process is implemented in ILSPA. Suppose the prop-

agation velocity is v, the coordinate of the impact load can beobtained as following:

x ¼ ðL� vDtÞ=2: ð3Þ

(6) ILEA 2 (Four point circularity localization algorithm)As described in Section 4.3, the coordinate of the AE source canbe obtained using Eq. (2).

In the evaluation four ILSPAs and two ILEAs are stored in theknowledge base of the impact load diagnostic agent. The knowl-edge base can be enriched by the learning ability of the agent inthe future.

5.1.2. The development of the inference engine for the diagnostic agentThe effective inference engine is the key part for the agent. The

inference engine of the impact load diagnostic agent determineswhat service ILSPA and ILEA register with Facilitator. It also deter-mines the suitable ILSPA and ILEA that the agent will automaticallychoose to realize the localization goal according to the signal of PZTSA and the user request.

To establish the inference engine, in the evaluation based on thefour ILSPAs and two ILEAs, numerous experiments are performedto localize the impact load position on three different materialplates. Five instances are researched on different material plates.The sensor distributions are as following: (1) On the glass fiber-epoxy composite plate, the distance between two PZT SAs is50 cm. (2) On the glass fiber-epoxy composite plate, the rectanglearea formed by four PZT SAs is 50 cm � 45 cm. (3) On the alumin-ium specimen, the distance between two PZT SAs is 49 cm. (4) Onthe aluminium specimen, the rectangle area formed by four PZTSAs is 49 cm � 45 cm. (5) On the carbon fiber specimen, the dis-tance between two PZT SAs is 15 cm. For the fiber-epoxy compos-ite and the carbon fiber composite material, within the suitabledistance, the propagation velocity is approximately supposed tobe equal in different directions. Tables 2 and 3 are part of theexperiment results.

Fig. 22. The user interface of the impact load diagnostic agent.


During the evaluation, experiment results show that when theimpact load is localized between two PZT SAs, the four ILSPAscan be used for three different materials. The localization accuracyof ILSPA4 (wavelet analysis) is the best, however the operating pro-cess of ILSPA1 (threshold setting) is the simplest. When the impactload is localized within the rectangle area formed by four PZT SAs,ILSPA1 can not be used. The localization result of the method is di-rectly relevant to the setting of the threshold. When the thresholdis different, the localization result can be widely divergent. ILSPA1is not suitable for the two-dimensional impact load monitoring.Since the propagation velocity is fast in the aluminium material,comparing to other three usually adopted time delay methods,only ILSPA4 can meet the demand for monitoring the position ofthe impact load in the two-dimensional area on the aluminiumplate. It is more accurate than the others in this occasion.

5.1.3. The implementation of the impact load diagnostic agentAfter the knowledge base and inference engine of the diagnostic

agent are constructed, each ILSPA register with Facilitator on whatservice it can provide (suitable for what material, suitable for ILEA1or ILEA2). Based on MATLAB tool (Matlab application, 2005), theimpact load diagnostic agent is realized. Fig. 22 is the user interfaceof the agent.

In the case of demonstration on the aluminium plate, since therequest is to monitor the impact load occurring in the two-dimen-sional area, the diagnostic agent will automatically choose waveletanalysis (ILSPA4) to localize the AE source as shown in Fig. 22.

5.2. The implementation of the whole multi-agent system

Based on the three implemented diagnostic agents, the wholemulti-agent system is realized. As introduced in the monitoringstrategies (part 3) of this paper, in the system there are differentequipments monitoring three typical kinds of structure states forthe large aluminium plate. Disp system is for sampling AE signalsfrom PZT SAs caused by the impact. PZT scanning system is forinterrogating multiple actuator–sensor channels since the cycle ac-tive monitoring method is presented to monitor the bolt loosening.Two kinds of strain SA are adopted to monitor the static load posi-tion including the Strain Gauge SA and Fiber optic SA. Micro opticssi425 is for monitoring the strain variation of the Fiber optic SA.The strain signal of Strain Gauge SA is monitored by the strain indi-cator and the wireless sensor developed in the lab. Fig. 23 is theschematic diagram for the system hardware integration. The com-

Table 2The localization result between two PZT SAs (unit: cm)

ILEA1 Fiber-epoxy (50 cm) Al

Actual coordinate 10 20 15ILSPA1 10.32 19.74 14ILSPA2 10.42 20.12 15ILSPA3 10.01 19.74 14

ILSPA4 10.06 19.99 14

Table 3The localization result within the rectangle area (unit: (cm,cm))

ILEA2 Fiber-epoxy (50 cm � 45 cm)

Actual coordinate (9.5,8) (�10.5,9ILSPA1 � �ILSPA2 (9.4,8.6) (�9.04,7ILSPA3 (9.6,8.8) (�11.9,9

ILSPA4 (9.6,8.6) (�9.8,9.0

munication among central coordination agent, central informationfusion agent, the impact load diagnostic agent, the bolt looseningdiagnostic agent and the static load diagnostic agent are realizedby Labview network communication technology (Internet applica-tions in Labview, 2000).

According to the ontology design in the past work (Zhao et al.,in press), the inter-communications among agents do not only con-vey the sensor data, but also convey other useful information in theform of attributes. The concept Sensor data with its data attributesis defined as follows: Sensor data [subsystem ID, sensor ID, sensoror actuator, static or dynamic, valid or invalid, data length, data].

� Subsystem ID refers to the subsystem that the sensor belongs to.� Sensor data ID refers to the kind of the sensor and its serial

number.� Sensor or actuator refers to the working status of the sensor. If it

is working at actuator status, this attribute is set to be 1, other-wise it is set to be 0.

� Static or dynamic refers to the style of the sensor signal. Whenthe sensor signal is static, this attribute is set to be 0, otherwiseit is set to be 1.

� Valid or invalid refers to the sensor output is valid or invalid.When the sensor is normal, this is set to be 1, otherwise it isset to 0.

� Data length refers to the length of the sensor data sampled.� Data refers to the actual data.

uminium (49 cm) Carbon-fiber (15 cm)

30 5 10.12 30.81 5.86 9.44.01 29.75 4.72 10.43.28 29.86 5.05 10.05

.98 30.16 4.95 10.24

Aluminium (49 cm � 45 cm)

) (12.5,7.5) (�10.5,7.5)� �

.27) � �

.6) � �

6) (12.78,7.11) (�10.6,6.91)

Fig. 23. The schematic diagram for the system hardware integration.


For example, Sensor data [1 and 2, PZT SA2,0,1,1,1024,1.7000,1.9000,2.3000,2.5000 . . .] means that the sensor locates at theboundary between subsystem 1 and 2, the sensor ID is PZT SA2,working at sensor status, dynamic signal, valid, every time sam-pling 1024 data, the data is 1.7000, 1.9000, 2.3000, 2.5000, etc.

Based on the ontology application the whole monitoring pro-cess has following steps:

Step 1: The CCR queries Sensor data and checks whether there isPZT SA working at actuator status. If there is, CCR automat-ically deals with bolt loosening monitoring process. ExceptPZT SA 7, 8, 9 as shown Fig. 9, the other PZT SAs on theboundary are self-organized to form a suitable sensor net-work to monitor the bolt loosening. The cycle active mon-itoring method is adopted. The web browser technology inLabview environment is adopted to realize the networkcommunication. Based on TCP protocol, CCR can monitorand control bolt loosening diagnostic agent remotely inreal time. The PZT scanning system samples the sensor dataon the spot and disseminates the monitoring data throughthe network. Table 4 is a part of the monitoring result. Thetable shows that bolt 1, 2, 3 is loosening respectively.

Step 2: If there is no PZT SA working at actuator status, the CCRchecks the trigger state of each PZT SA. If there is a trigger,the CCR chooses the four PZT SAs which have the triggerfirst and begins the impact load localization process inthe subsystem which includes these four PZT SAs. Theimpact load diagnostic agent begins to work. SuitableILSPA and ILEA are automatically called. For example,when there is an impact at substructure 3 as shown inFig. 24, the four AE signals that the four PZT SAs of thissubstructure sampled by the Disp AE system are shownin Fig. 25. ILSPA4 is automatically called. The four corre-

Table 4The Euclidean distance of different modes

Mode 0 1 2 3 4 5 6 7 8 9 10

1 2.36 1.03 2.54 1.35 2.15 1.43 1.91 1.86 1.75 2.54 2.332 2.01 3.18 0.74 1.98 3.18 2.61 2.67 1.58 3.24 2.56 2.58

3 2.31 2.20 2.21 1.28 2.70 2.08 2.21 2.25 2.35 2.45 2.15

sponding signals after Gabor wavelet transform are shownin Fig. 26. In the evaluation the propagation velocity of A0

mode at 180 kHz in the aluminium material is 2800 m/s (Jiao et al., 2005). The position of the impact load on thelarge aluminum is localized after ILEA2 operation.

Step 3: if there is no trigger, the CCR begins the static load positionmonitoring process.

Based on multi-agent technology, the system can fuse the signalfrom different sensors and different equipments to monitor thestatic load position in every substructure and on the boundary be-tween adjacent substructures through the cooperation of differentsubsystems. For the wireless sensor, the relationship between theoutput of the voltage change DV and the strain parameter variationDe is as following: DV ¼ K

4 � De � V . K is the sensitivity coefficient ofthe strain gauge, K = 2. V is the bridge voltage provided, V = 3v. ForFBG, the relationship between the change of the FBG central wave-length Dk and the strain change De is as following: Dk

De ¼ 1:2 nm1000 le :

Every time each subsystem reads the data of the four strain var-iation of strain SAs and stores them in the temporary base of eachsharing information management agent (SIMA). If SIMA finds themaximum change between fixed base and temporary base doesnot exceed 20 l e, SLSPA and SLEA will not be called. The small

Fig. 24. Impact load localization monitoring.

0 100 200 300 400-1

-0.5

0

0.5

1

0 100 200 300 400-0.5

0

0.5

1

0 100 200 300 400-1

-0.5

0

0.5

1

0 100 200 300 400-1

-0.5

0

0.5

1

Time (us)

Time (us)Time (us)

Time (us)

Am

plit

ud

e (

v)A

mp

litu

de

(v)

Am

plit

ud

e (

v)A

mp

litu

de

(v)

Fig. 25. Four AE signals received in the substructure 3.

0 100 200 300 4000

0.5

1

1.5

2

2.5

0 100 200 300 4000

0.5

1

1.5

0 100 200 300 4000

0.5

1

1.5

2

0 100 200 300 4000

0.5

1

1.5

Time (us)

Time (us)Time (us)

Time (us)

Am

plitu

de (

coef

f)

Am

plitu

de (

coef

f)

Am

plitu

de (

coef

f)

Am

plitu

de (

coef

f)

Fig. 26. Four corresponding AE signals after Gabor wavelet.


change is considered as environment influence or noise. The SIMAof every subsystem will update the data in the fixed base with thedata in the temporary base. The data stored in the fixed base ofSIMA is updated with an interval of 1 s to eliminate the environ-ment influences such as temperature influence and sensor perfor-mance shifting. If the SIMA finds the data change is bigger enough,it means the 70 N load has been applied on the structure. Thethreshold 20 le is set according to the finite element modelinganalysis based on MSC.Patran/Nastran software (MSC, 2001) andpractical loading experiments. When the SIMA finds there is thestrain variation exceeding 20 l e, it reports this situation to CCR.The CCR will call the suitable strain SAs network to deal with thestatic load localization. The process includes following steps. Step1: In every subsystem, the sum of the four absolute values of thestrain change (SumðiÞ ¼ jDei1 j þ jDei2 j þ jDei3 j þ jDei4 j, i is the sub-

system ID) is calculated. If the sum exceeds 280 le in one subsys-tem, the SLEA of the subsystem is called to localize the static loadposition. The SLEAs of other subsystems are not called. The param-eter 280 le is also set by the finite element modeling analysis andpractical experiments. Step 2: If there is no sum value exceeding280 le, the SLEA of each subsystem is called. The load is supposedto be on the subsystem with the minimal Euclidean distance. Thesmaller the minimal Euclidean distance is, the more accurate thediagnostic result of the subsystem with the minimal distance is.After the calculation of Eq. (1), if the ratio between the minimalEuclidean distance and the second minimal distance exceeds 10 le,the diagnostic result of the subsystem with the minimum di is thefinal result. The parameter 10 is determined after lots of experi-ments. Step 3: If the ratio of the two minimal Euclidean distancesis less than 10, it is considered that neither of the two subsystems

Fig. 27. The static load localization on the boundary between two subsystems.


develops successful localization. The static load position is sup-posed to be on the boundary between two adjacent subsystems.A new sensor network is organized. For example, when the staticload appears at subarea 731 as shown in Fig. 27. Table 5 is the cor-responding strain variation in the four subsystems. It belongs toStep 3 above. Table 6 is the minimum Euclidean distance in thefour subsystems. Since 420/70 < 10, the new sensor network is or-ganized to get the result that the static load is on the boundary be-tween subsystem 7 and 3. The subarea ID of the boundary is 731.Table 7 is the diagnostic result after the sensor network organized.The work flow of the static load localization process is shown inFig. 28. The rate of accuracy of the static load localization is 95.2%.

Accomplishing the steps above, the system can deal with theentire monitoring process automatically.

5.3. User interface agent

The user interface is shown in Fig. 29 for the whole multi-agentsystem of the evaluation work.

Table 5Strain variation of the strain gauges in the four subsystems (unit: le)

Strain Gauge ID Subsystem 2 Subsystem 3 Subsystem 6 Subsystem 7

1 0 56 �12 �232 12 �39 12 373 �4 13 6 12

4 31 98 60 45

Table 6The minimum Euclidean distance (EDmin) in the four subsystems

Subsystem 2 Subsystem 3 Subsystem 6 Subsystem 7

EDmin 70 420 998 2238

Table 7The minimum Euclidean distance (EDmin) for the new network

Subsystem 6–2 Subsystem 7–3

EDmin 1137 3

5.4. Discussion

The evaluation work shows that using multi-agent technologyfor the integration of the large practical structural health monitor-ing system, the following advantages can be achieved:

(1) The whole system can deal with geographically distributednetworks, fuse different kinds of sensor data, fuse differentmonitoring methods and give an efficient evaluation resultof the large structure using uncomplicated logic.

(2) According to the sensor information, corresponding signalprocessing methods and damage evaluation methods canbe chosen automatically. Through collaboration, theunhealthy factor of the large plate can be identified in asemi-autonomous mode.

(3) Suitable sensor network can be self-organized. Useless datacan be automatically discarded. This help to decrease theamount of system computing.

(4) Using the agent learning memory realized by SIMA, the sen-sor performance shifting and the influence from environ-ment parameters to the sensor can be eliminated. Usingthe agent learning ability of the diagnostic agent, the newdiagnostic experience can be added to the knowledge baseand the efficiency of the whole system will be improved inthe future.

6. Conclusions

This paper shows the efficiency of the multi-agent system forthe large structural health monitoring. In the future work,negotiation among different agents will be researched in detail.More validation work of the multi-agent technology for the largepractical structural health monitoring will be conducted, such asthe integration monitoring work on the actual wing box ofaircraft.

Acknowledgements

This work is supported by Natural Science Foundation of China(Grant Nos. 50278029 and 50420120133), Aeronautic ScienceFoundation of China (Grant Nos. 04A52002 and 20060952), Pro-gram for New Century Excellent Talents in University (Grant No.NCET-04-0513).

CCR checks if there is PZT SA

Working at actuator status

N

Strain change >=20 με ?

CIFA UIA

Strain SA data are read to suitable SIMA

Y

N

Data in SIMA updated

Sum(i)>=280 με ?Y

SLEAi is called to localize

The static load position

N

In every subsystem, SLEA is called

?10min

min ≥i

j

dd

Y

SLEAi is called to localize

The static load position

N

The new sensor network is organized to

localize the static load position

Report the result to CIFA

Fig. 28. The workflow of the static load diagnostic agent.

Fig. 29. The user interface of the multi-agent system.



References

Boller, C., & Buderath, M. (2007). Fatigue in aerostrcutures – where structural healthmonitoring can contribute to a complex subject. Philosophical Transactions of theRoyal Society A, 365, 561–587. doi:10.1098/rsta.2006.1924.

Internet applications in Labview, Jeffrey Travis, Prentice Hall PTR (2000). http://sthmac.magnet.fsu.edu/labview/basic_labviewbooks.html.

Jennings, Nicholas R. (1998). A roadmap of agent research and development.Autonoumous Agents and Multi-agent Systems, 1(1), 7–38.

Jiao, J., He, C., & Wu, B. (2005). A new acoustic emission source location techniquebased on wavelet transform and mode analysis. Chinese Journal of ScientificInstrument, 26(5), 482–485.

Marsh, David, Tynan, Richard, & O’Kane, Donal (2004). Autonomic wireless sensornetworks. Engineering Applications of Artificial Intelligence, 17, 741–748.

Matlab application (2005). Publishing house of electric industry.MSC (2001). Software corporation, MSC.Nastran quick reference guide.Sohn, H., Farrar, C. R., Hemez, F. M., Czarnecki, J. J., Shunk, D. D., Stinemates, D. W., et

al. (2003). A review of structural health monitoring literature: 1996–2001. LosAlamos National Laboratory Report, LA-13976-MS.

Tao, Baoqi (1997). Smart materials and structures. Beijing: National Defense IndustryPress.

Van Dyke Parunak, H. (2000). A practitioners’ review of industrial agentapplications. Autonomous Agents and Multi-agent Systems, 3, 389–407.

Wang, Minhong, Wang, Huaiqing, & Xu, Dongming (2005). The design of intelligentworkflow monitoring with agent technology. Knowledge-Based Systems, 18(1),257–266.

Wooldridge, Michael, & Jennings, Nicholas R. (1995). Intelligent agents: theory andpractice. Knowledge Engineering Review, 10(2), 115–152.

Yuan, Shenfang, Lai, Xiaosong, Zhao, Xia, Xu, Xin, & Zhang, Liang (2006). Distributedstructural health monitoring system based on smart wireless sensor and multi-agent technology. Smart Materials and Structures, 15(1), 1–8.

Yuan, Shenfang, Xu, Ying di, & peng, Ge (2004). New developments in structuralhealth monitoring based on diagnostic Lamb wave. Journal of Material Scienceand Technology, 20(5), 490–496.

Xia Zhao, Shenfang Yuan, & Zhenhua Yu (in press). Designing strategy for multi-agent system based large structural health monitoring. Expert Systems WithApplications, 35(1). doi.org/10.1016/j.eswa.2006.12.022.

Yu, Zhenfeng, Yang, Zhichun, & Shu, Zhongping (2006). Experiment study ofstructural damage detection using cross correlation function amplitude vectors.Mechanical Science and Technology, 25(11), 1264–1267.

Zig Bee Alliance. ZigBee Specifications v1.0[EB/OL]. http://www.zigbee.org/, 2006-2-7.

http://dx.doi.org/10.1098/rsta.2006.1924

http://sthmac.magnet.fsu.edu/labview/basic_labviewbooks.html

http://sthmac.magnet.fsu.edu/labview/basic_labviewbooks.html

http://www.zigbee.org/

an evaluation on the multi-agent system based structural health monitoring for large scale...

Documents