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Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring
Expert Systems with Applications 37 (2010) 2028–2036
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring Jian Wu a, Shenfang Yuan a,*, Sai Ji b, Genyuan Zhou c, Yang Wang a, Zilong Wang a a
The Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29# Yu Dao Street, Nanjing 210016, PR China Nanjing University of Information Science and Technology, 219# Ningliu Road, Nanjing 210044, PR China c Jinangsu Teachers University of Technology, 1801# Zhongwu Road, Changzhou 213001, PR China b
a r t i c l e
i n f o
Keywords: Multi-agent Wireless sensor network Structural health monitoring
a b s t r a c t Much attention has been focused on the research of structural health monitoring (SHM), since it could increase the safety and reduce the maintenance costs of engineering structures. In recent years, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based SHM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of six different agents for SHM applications, the distributed wireless sensor network can automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an experimental aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multi-agents software architecture is defined. The multi-agent monitoring principle and implementation in the validation work for two typical kinds of structure states are presented. This paper shows the efficiency of the multi-agent technology for WSN based the SHM applications on the large aircraft structures. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction The performance of the in-service aerospace structures can be affected by degradation resulting from exposure to harsh flight environment conditions or damages resulting from external conditions, such as impact, loading, operator abuse or neglect. In order to improve the safety level of the aerospace structures, structural health monitoring (SHM) is researched for devoted to predict the onset of damage and deterioration in aircraft structural condition with the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors. Traditionally, the cable-based SHM systems for aircraft structures might involve large number of wires employed for communication among sensors and centralized data acquisition systems. If the SHM system designed for a large structure is comprised of a large number of sensors and address stringent real-time operations, the system can become overburdened with computational tasks. In response to the cumbersome wires and performance shortcomings of centralized cable-based SHM systems, wireless sensor network (WSN) has been explored for adoption in recent years (Caffrey * Corresponding author. E-mail address: [email protected] (S. Yuan). 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.06.098
et al., 2004; Lynch et al., 2003; Straser & Kiremidjian, 1998; Xu et al., 2004). For SHM system applied on large scale structure, different kinds of density sensor networks are required to monitor different structure parameters, such as stress, stain, displacement, acoustic, pressure, temperature and etc. These sensors with different theories and functions might be connected to the wireless sensor nodes for data acquisition and processing. Many benefits can be gained from wireless SHM system, such as local computational ability, low cost deployment, and wireless networking functionality. However, the information obtained by each sensor node is limited, so does the local signal processing ability of each sensor node. Besides, real large scale aircraft structures are complicated to be estimated. Hence, when implementing a wireless SHM system for practical big aircraft structures, the challenge might includes how to integrate dedicated wireless SHM sensor nodes; how to coordinate and manage the large dense wireless SHM network, since the SHM systems for practical aircraft structures need multi-point, heterogeneous, and synchronous operations. Multi-agent technology over the past few years has come to be perceived as crucial technology not only for effectively exploiting the increasing availability of diverse, heterogeneous and distributed on-line information sources, but also as a framework for
J. Wu et al. / Expert Systems with Applications 37 (2010) 2028–2036
building large, complex and robust distributed information processing systems which exploit the efficiencies of organized behavior. Given the general benefits of multi-agents, scholars have explored the possibility for sensor network applications (Helvik & Wittner, 2001; Kumar, Shepherd, & Zhao, 2002; Qi, Iyengar, & Chakrabarty, 2001). A design method for MAS based SHM system has been presented by the authors (Yuan, Lai, & Zhao, 2006). This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. The collaborative multi-agent wireless sensor network architecture including the physical and software layers is presented. Six cooperation agents for SHM applications are defined based on the clustered wireless sensor network which could automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an aircraft aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multiagent software implementations are introduced in the validation work for two typical kinds of structure states. The evaluation system and experimental result shows the efficiency of the multiagent technology for WSN based SHM applications on large aircraft structures.
2. Collaborative MA-WSNs architecture based SHM To apply the multi-agent technology to the distributed WSN based SHM applications, a multi-agent architecture for sensor networks that can support collaborative SHM task allocation, network self-organization and sensor data aggregation is described from both physical and software architecture perspectives. 2.1. Physical architecture In SHM applications, groups of sensors are deployed in specific areas to observe quantities as stress, stain, acoustic or pressure and report to a remote central station in a multi-hop fashion. Sensors are typically grouped around specific points of interest in a component of the structure. From a network perspective, the nodes connected to these sensors might be deployed to clustered topologies, and although the size and the position of these clusters may vary significantly for different SHM applications, this similarity allows us to create protocols that can be effective over all these SHM applications. In the clusters, cluster heads can communicate with any other sensor nodes within its radio range to a star topology which might support synchronization simply. In the top layer, the station management node communicates with the cluster heads to a star topology. The clustered topology framework can completely support low-power, multi-point, and heterogeneous operations with a distributed mechanism. At the physical level, the sensor network consists of the following types of nodes: Sensor Nodes (SN): These are the smart network devices that communicate wirelessly and can be connected to sensors for structure monitoring. Based on the applications, any sensor node might act as the cluster head in their cluster. Cluster Heads (CH): These are the smart network devices that act as data aggregation and transmission, based on SHM application necessities. In the clusters, cluster heads can communicate with any other sensor nodes within its radio range to a star topology which might support synchronization simply. Station Management Node (SMN): The station management node is more powerful desktop workstation that has wireless links
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to the sensor nodes and communicates with the cluster heads to a cluster-star framework which can completely support low-power, multi-point, and heterogeneous operations with a distributed synchronization mechanism. They manage a network of sensor nodes, representing an application defined structure region. 2.2. Software architecture Architecturally, the sensor network software consists of two components, namely, sensor node (SN) software and station management nodes (SMN) software. The SN software contains a sensor manager that manages and measures the local sensor data. The SMN Software additionally provides data mining and management facilities over the regional sensor data and a code repository of useful task executables that can be ported to the sensor nodes on demand. The multi-agent system based SHM (Yuan et al., 2006) provides an information collaborating architecture for SHM application. This paper extends this model and defines an agent-based framework that can be useful for collaborative information processing in wireless sensor networks. The framework facilitates the integration of the hardware agents of the SHM application with the software agents. In this model, a sensor network application involves the following six types of software agents: 2.2.1. Structure monitoring agents (SMA) The SMA resides on a sensor node and has access to the monitoring data source of a sensor node in that region. It makes the sensor information of the represented sensor node available to the SHM application. In the architecture, there is a structure monitoring agents for every sensor node. In active SHM applications, the SMA might be act as the excitation signal provider. 2.2.2. Data manager agents (DMA) The main task of DMA is to manage and mine the regional sensor data. DMA can also be extended to support clustering, teaming, and routing. The structure monitoring agent registers itself with the data manager agent of the node on which it resides. 2.2.3. SHM application agents (SAA) The SAA is a mobile agent that is specific to the SHM application. It can possibly communicate, move, cooperate, reason, adapt, learn and perform other application specific tasks. It consists of four components, namely, ‘‘identification” to uniquely identify the mobile agent, ‘‘data space” to store intermediate integration results, ‘‘method” that hosts different integration algorithms, and ‘‘itinerary” that the mobile agent follows during migration. 2.2.4. Translation agents (TA) Its main task is to translate SHM application specifications into controllers for local nodes, such that the node behavior is consistent with the application-specific and platform-adaptive global network behavior. 2.2.5. Central collaboration agent (CCA) The CCA is implemented by software in the SMN to collaborate with DMAs in CHs for the whole monitoring process and fuse the different information from the different SAAs to obtain the most reliable and precise conclusion. It decides when to communicate with DMAs to dispatch and manage SAAs. 2.2.6. User interface agent (UIA) The UIA is implemented by the software in the SMN and the monitor of the computer system to accept commands from the user and show the monitoring results to the user.
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Of all the six types of agents, the sensor node may host SMA, DMA, TA and SAA, while the SMN mainly hosts SAA, CCA and UIA. Fig. 1 illustrates the multi-agent architecture for collaborative WSN based SHM applications and interrelationship among different agents. The design of this architecture is according to the following consideration. Considering a typical SHM system, the sensors are embedded in or bonded to the structure to sense the structural parameters. Appropriate signal or information processing methods are adopted to analyze and extract the features sensitive to the structural damage extracted from the sensed data. The local health status of the structure can be deduced using corresponding damage evaluation methods. Three kinds of agents are defined according to the three different functions. They are structure monitoring agents (SMA), data manager agents (DMA) and SHM application agents (SAA). The architectural framework supports agent mobility for SHM applications. Migration of a SAA from source to destination may trigger updates for the DMA and CCA agents. Among the four components of the SSA, the most important is the mobile agent’s itinerary. Itinerary can be determined either statically or dynamically. That is, it can be calculated either before the agent is dispatched or while the agent is migrating. Dynamic itinerary planning is more flexible, and can adapt to sensor data changing in real time. A large scale structure can be divided into some subsystems to be monitored and analyzed. When user is interested some region of the monitored structure, statically itinerary planning for SAA is created and decided by the interested position of the structure component. Static and dynamical itinerary planning in our architecture will be validated in the next section.
monitored by sensor arrays and indicate structural damages are researched such as joint failure and strain distribution change. 3.1. Evaluation system setup The setup of the practical system is shown in Fig. 2. The structure is a 2 m 1.2 m aviation hard aluminum (the type is LY12) plate with 2.5 mm thick, fastened to a steel frame by 64 bolts. The bolts are deployed around the frame with a distance of 100 mm. Except for the edge area with 100 mm 110 mm area to arrange the bolts, the whole structure is divided averagely into eight substructures with the dimension of 450 mm 490 mm. Each substructure is divided into 9 sub-areas which are all embedded four strain gauges. The dimension of every sub-area is 150 mm 140 mm. 12 piezoelectric ceramic sensors with 10 mm diameter are fixed on the corners of each substructure. Based on the designed multi-agent wireless sensor network architecture, this evaluation system should demonstrate following functions: the sensor network can automatically choose sensor nodes, selforganize wireless sensor network clusters, integrate suitable sensor data to localize the joint failure around the frame, and the static load on the structure which cause the strain distribution change in the structure. To have a better show of the advantages of the multiagent for wireless sensor network collaboration in SHM applications, two monitoring cases including monitoring the static load happened in the substructures by mobile agent SAA migrations and cycle active monitoring the joint failure avoiding pattern overlapping are demonstrated.
3. Multi-agent WSN architecture evaluation for SHM
3.2. Wireless sensor network platform development for the evaluation system
In order to validate the efficiency of the multi-agent WSN architecture for SHM, an aircraft plate structure as the typical engineering structure is adopted. Two typical structure states which may be
Because the performance of the entire wireless SHM system is dependent upon the individual wireless sensor nodes, one of the fundamental studies of the evaluation system is to design the
Sensor Nodes (SNs)
Interest Regions in SHM Stations Management Node (SMN)
SMA
DMA User Interface Agent
TA
SAA Cluster Heads (CHs)
Central Collaboration Agent
DMA TA Sensor Nodes (SNs) SMA
DMA
TA
SAA
Sensor Nodes (SNs)
DMA TA
Cluster Heads (CHs) Sensor Nodes (SNs)
SMA: Structure Monitoring Agents DMA: Data Manager Agents
SMA
SMA
SAA
SAA
SAA: SHM Application Agents DMA
TA
TA: Translation Agents : The migration of SAA
Fig. 1. The multi-agent architecture for collaborative WSN based SHM.
J. Wu et al. / Expert Systems with Applications 37 (2010) 2028–2036
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Fig. 2. The picture of the evaluation structure and system setup.
Fig. 3. The schematic circuit diagram for conditioning the strain gauge sensor signal.
dedicated wireless sensor nodes for structure sensors connection. PZT sensors and strain gauges are typical structure sensors in SHM applications. In this paper, two dedicated sensor nodes are designed for strain distribution changes and joint failure monitoring. Based on the generalized wireless sensor node architecture (Hill, 2003), the design of the wireless sensor node is divided into three functional modules: sensor input unit, processing core, and wireless communication. Detailed design information on the processing core and wireless communication is outlined in our previous paper (Wu, Yuan, Zhao, Yin, & Ye, 2007). Specifically in the processing core design of this paper, the TI MSP430F1611 MCU is chosen instead of MEGA128L. Specifically in the wireless communication design, the TI CC2420 RF transceiver is chosen instead of CC1000. CC2420 is a true single-chip 2.4 GHz IEEE 802.15.4 compliant RF transceiver designed for low-power and low-voltage wireless applications. With 2.4 GHz RF frequency and 25 dB m output power, the wireless transceiver only draws 8.5 mA of current while actively transmitting, guaranteeing the low-power character of the designed wireless node. 3.2.1. A Wireless sensor nodes design for strain measurement Considering high-precision strain distribution monitoring requirement, the strain gauge signal conditioning circuit is designed for the sensor input unit of wireless strain nodes. The circuit is composed of three parts, the bridge circuit, the amplifier and the output circuit. The conditioning circuit provides a 3 V high-precision power supply for the bridge circuit. The bridge circuit output
Fig. 4. (a) The circuit design schematic for a sinusoidal signals actuator circuit; and (b) the circuit design schematic for PZT signals conditioning.
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Fig. 5. The picture of the designed wireless strain node, PZT node, station node and shock-proof encapsulation.
corresponds to structural strain monitored. Since the sensitivity of strain gauge is low, the instrumentation amplifier AD623 is adopted to amplifier the bridge circuit output. As a low-power instrumentation amplifier, the AD623 can offer excellent accuracy for the sensor input unit of the wireless sensor nodes. The strain gauges are usually adopted to monitor static signal, thus a lowpass filter is designed to eliminate the high frequency noise. The voltage follower is adopted to output the filtered voltage signal. Fig. 3 shows the schematic circuit diagram for conditioning the strain gauge sensor signal. Another hardware component of the strain sensor input unit is an analog-to-digital (A/D) converter that is chosen to accommodate external sensors with analog outputs. With greater than 200 ksps maximum conversion rate, the 12-bit analog-to-digital conversion integrated in MSP430F1611 MCU has eight individually configurable external input channels. 3.2.2. B Wireless sensor nodes design for joint failure active monitoring The joint failure monitoring is based on the structural vibration response of PZT sensors which can act both as the actuator or the
sensor. Based on this principle, the sinusoidal signal actuating and conditioning amplifier circuit for PZT sensors are designed. Fig. 4a shows the circuit design schematic for a sinusoidal signals actuator circuit, and Fig. 4b shows the circuit design schematic for a PZT signal conditioning circuit in the evaluation system. It can generate the 10 V peak-to-peak sinusoidal signal with the center frequency tunable from 0 to 120 KHz. Care is taken to select timing circuitry with very low standby current and fast start-up to meet the timing and limited power budget requirements. The design uses the timer TCL555: a low-power timer for the 0.02 ms pulse period. The resonant frequency is chosen near the operating frequency as a compromise between circulating current losses and sensitivity to variations in the effective load capacitance. A low-power amplifier chip OPA340 is adopted in the PZT signal amplifier circuit. Since the ADC module of MSP430F1611 can collect the analog signal from 0 V to Vcc which is supplied for 3 V, the output voltage amplitude of the voltage amplifier should at the range from 0 to 3 V. The signals can be converted right voltage by the addition circuit, and then be magnified through a two-stage amplifier circuit. Its magnification times are tunable from 50 to 100. Between the two-stage amplifier-circuits, there is a low-pass filter circuit in every series which can get rid of the high-frequency interference. 3.2.3. C Wireless station nodes design for the evaluation system This paper uses a USB controller IC from FTDI company to design the wireless station node for communicate with the host computer. Multiple wireless station nodes may be connected to a single computer’s USB ports at the same time. Each station node will receive a different COM port identifier. In the evaluation system, one station node is connected. UIA may read data and send instructions by the opening the COM port assigned to the station node which communicates with the host PC through USART1 on the MCU. After the design and IC choice of the circuit components, the 3 four-layer printed circuit boards for the three modularities and the dedicated installation box are designed and fabricated respectively. The reason to adopt the four-layer circuits and divided design is to sufficiently separate analog and digital circuit components. The other benefit gained from the design is that each part can easily be upgraded as advanced IC technologies and
Fig. 6. The sensor network deployed for the collaborative static load localization.
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different applications requirements. Fig. 5 shows the picture of the designed wireless strain node, PZT node and shock-proof encapsulation for lightweight installation. The power to supply the wire-
less sensor node is designed to use 3 V direct current (DC) power since all the components are low-power. Thus, two normal 1.5 V batteries can power the complete wireless sensor node.
CCA
DMA Monitoring time interval arrived? Periodic triggered tasks
New SAAs is added? User triggered tasks
Y
Y
SMAs of 4 sensor nodes collect
SAAs migrate toward to the
and store the strain variation data
destination CHs
DMA
Any Strain change >=20 με ? N Y DMA create a SAA
SAA complete migration in the cluster and integrate all strain data to its data space in the CH
CCA aggregate data from SAAs
UIA show the monitoring result Fig. 7. The workflow of the multi-agent implementation for static load localization.
Fig. 8. The sensor network deployed for cycle active monitoring of bolt loosening.
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3.3. Multi-agent implementation for collaborative static load localization The static load localization is based on the strain distribution variation monitored by the wireless strain sensor network nodes. As shown in Fig. 6, the sensor network is deployed by 16 sensor nodes, respectively connected to 16 strain gauges in four same dimension substructures I, II, III and IV. In this section, six types of software agents are implemented for the collaborative static load localization in the above-mentioned strain sensor network. Structure Monitoring Agents (SMA) is designed to access to the monitoring strain data source of its own sensor node. It makes the strain sensor information of the represented sensor node available
to the collaborative static load localization. Data Manager Agents (DMA) has two main tasks in the evaluation system. One is to decide when start to attach itself to the sensor nodes clustering. The other task is to decide the generation and migration of the SHM Application Agents (SAA). In the collaborative static load localization of the evaluation system, SHM Application Agents (SAA) act as a mobile agent that is specific to static load localization tasks. Their identifications are made up of the sensor node ID which monitor the strain data change and start the localization task. Their data spaces are used for storing intermediate data integration results. The methods host different pattern matching algorithms for load localizations. The itineraries save the next routing destination of SAA. Translation Agents (TA) is designed to the TinyOS function
DMA Monitoring time interval arrived? Periodic triggered tasks
CCA New SAAs is added? User triggered tasks
Y
Y
SMA of the i sensor node actuate the
The SAA itinerary is updated by the
connected PZT (i = 1 to 12)
destination nodes ID, including j, k ... z (1≤ j, k ... z ≤ the maximal nodes ID).
SMAs of the two sideward sensor nodes collect the peak values of the corresponding PZT signal.
DMA Any peak value change >=0.2 V ?
N
Y If there is no SAA, DMA of the i sensor node create a SAA
SAA integrate peak values of two adjacent nodes to its data space in the i sensor node; If SAA is desiccated to Periodic triggered task, “i = i +1” will be used for updating the itinerary of SAA.
Y
SAA i the maximal nodes number +1 ? orSAA do not complete the migration ?
N CCA aggregate data from SAAs
UIA show the monitoring result Fig. 9. The workflow of the multi-agent implementation for cycle active monitoring of bolt loosening.
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for strain data collection and processing by controllers of local nodes. Central Collaboration agent (CCA) and User interface agent (UIA) are implemented by Delphi development platform in the SMN to collaborate, monitor and control the whole monitoring process. In this paper, a loading equipment is used to change the strain distribution in the plate. 70N is chosen in the demonstration. When the concentrated load is applied on the structure or the applied position changes, the strain distribution changes correspondingly and the output mode of the strain sensor nodes changes too. The SAA classifies the different pattern data collected by SMA to decide the different load position. The pattern recognition method that the SAA adopted is the minimum-distance classification. The distance between two patterns is calculated using the Euclidean distance, shown in Eq. (1):
" dðx; yÞ ¼
n X
Table 1 Strain data from SN9 to SN12 in the substructure III (unit:
le).
Loading locallization SN
No load
1
2
3
4
5
6
7
8
9
9 10 11 12
0 0 0 0
18 269 37 8
18 36 46 45
4 23 25 320
25 0 39 10
32 3 50 4
16 8 45 9
272 25 106 68
80 7 121 35
33 8 324 31
#1=2 2
bxi yi c
ð1Þ
i¼1
where x; y indicate two different patterns, xi refers to the strain monitored by each SAA and yi refers to the reference strain stored in the data base related to different load position. Fig. 7 shows the workflow of the multi-agent implementation process for static load localization. The multi-agent implementation process for collaborative static load localization includes following steps. With a monitoring time interval, 4 SMAs of 4 sensor nodes in each substructure collect and store the strain variation data synchronously. Then if any strain variation exceeds 20le, DMAs creates a SAA and select a CHs in the 4 sensor nodes according to the residual energy. The threshold 20le is set according to the finite element modeling analysis based on MSC. Patran/ Nastran software and practical loading experiments. SAA will complete all strain data integration to its data space in the CH of the substructure. Finally the CCA and UIA will aggregate all triggered SAAs from CHs and show the integrated result about the structure situation. Besides the periodic monitoring, the users might want to know the structure state at any moment. In these cases, SAAs may be added by the CCAs and distributed to the CHs in the objective structure. Then the SAAs will act as the cooperation work like the periodic monitoring cases. 3.4. Multi-agent implementation for joint failure active monitoring The bolt loosening monitoring is based on the structural vibration response. Considering numerous structure joint bolts and existent pattern overlapping, cycle active monitoring method is designed for multi-agent wireless sensor network cooperation in the paper. In Fig. 8, the sensor nodes connected by PZT sensors around the boundary might act as actuator and CH in turn. Each time the actuator and the two sideward sensor nodes (left and right) form a cluster used for exactly monitoring adjacent bolts loosening. For example, as shown in Fig. 8, while SN 1 acts as the actuator, SN2 and SN6 will work as the sensor nodes. In this case, DMA in SN1 will create a SAA for integrating the structural vibration data of SN2 and SN6. In turn, while SN12 acts as the actuator, the SAA will migrate to SN12 and keep on aggregating the structural vibration data of SN1 and SN11 to its data space. Fig. 9 shows the workflow of the multi-agent implementation process for cycle active monitoring of bolt loosening. The multiagent implementation process for joint failure active monitoring includes following steps. When the periodic triggered tasks is allocated for joint failure monitoring, SMA of the sensor nodes which ID is 1 will periodically actuate the PZT and make the SMAs of the two adjacent nodes collect the peak values of the corresponding PZT signal synchronously. In this case, dynamical itinerary plan-
Fig. 10. The interface of the UIA implementation.
ning is designed for SAA migration. Then if any peak value exceeds 0.2 V, DMAs will create a SAA and integrate peak values of two adjacent nodes to its data space in the i sensor node. The threshold 0.2 V is set according to the finite practical bolt loosening experiments. Then ‘‘i = i + 1” will be used for updating the itinerary of SAA. SAA will complete all PZT data integration to its data space from SN1 to SN2 in the evaluation system. Finally the CCA and UIA will aggregate all PZT feature data and show the integrated result about the joint failure situation. If the users want to know the partial blots loosening states at any moment, a SAA added by the CCA might update its itinerary by the destination nodes ID, including j, k, . . . , z ð1 6 j; k; . . . ; z 6 the maximal nodes IDÞ. In this case, static itinerary planning is designed for SAA migration. Then SAAs for user triggered tasks will act as the same cooperation work during its migration like the periodic triggered tasks. 3.5. Evaluation experiment results Fig. 10 show the designed interface for UIA implementations. For the wireless strain node, the relationship between the output of the voltage change DVand the strain parameter variation De is as following: DV ¼ K4 De V. K is the sensitivity coefficient of the strain gauge, K ¼ 2. V is the bridge voltage provided, V ¼ 3v : Table 1 shows the strain data from SN9 to SN12 in the substructure III stored in the UIA. These strain data could be used for the trained data to decision making of static load localization by CCA. Table 2 lists a part of the monitoring result when bolt 1, 2, 3 is loosening, respectively. 4. Conclusions The evaluation work shows that multi-agent WSN based SHM could gain the following advantages. The collaborative multi-agent
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Table 2 The Euclidean distance of different modes. Mode
0
1
2
3
4
5
6
7
8
9
10
1 2 3
2.36 2.01 2.31
1.03 3.18 2.20
2.54 0.74 2.21
1.35 1.98 1.28
2.15 3.18 2.70
1.43 2.61 2.08
1.91 2.67 2.21
1.86 1.58 2.25
1.75 3.24 2.35
2.54 2.56 2.45
2.33 2.58 2.15
wireless sensor network architecture could automatically allocate SHM tasks by the mobile agent (SAA). According to different SHM task trigger mode, such as periodic triggered tasks and user triggered tasks, static and dynamical itinerary planning might be used for the design of the itinerary of SAA. Clustered sensor network can be changed and self-organized by SAA during its migration. The distributed data integration of SAA could help to decrease the amount of packages transmission and energy dissipation. By adding the suitable threshold in SAA implementation, the sensor performance shifting and the influence from environment parameters to the sensor can be eliminated. Acknowledgements This work is supported by Natural Science Foundation of China (Grant Nos. 60772072 and 50830201), National High-Tech Research and Development Plan (863 Plan). (Grant No. 2007AA03Z117). References Caffrey, J., Govindan, R., Johnson, E., Krishnamachari, B., Masri, S., & Sukhatme, G., et al. (2004). Networked sensing for structural health monitoring. In Proceedings of the fourth international workshop on structural control (pp. 57–66), June 10–11, New York, NY.
Helvik, B. E., & Wittner, O. (2001). Using the cross-entropy method to guide/govern mobile agent’s path finding in networks. In Proceedings of third international workshop on mobile agents for telecommunication applications, 2001 (pp. 255– 268). Hill, J., (2003). System architecture for wireless sensor networks. Ph.D. thesis, UC Berkeley. Kumar, S., Shepherd, D., & Zhao, F. (2002). Collaborative signal and information processing in micro-sensor networks. IEEE Signal Processing Magazine, 19(2), 13–14. Lynch, J. P., Sundararajan, A., Law, K., Kiremidjian, A., Carryer, E., & Sohn, H. (2003). Field validation of a wireless structural monitoring system on the Alamosa Canyon Bridge. In Smart structures and materials 2003: Smart systems and nondestructive evaluation for civil infrastructures spie’s 10th annual international symposium on smart structures and materials (Vol. 5057, pp 267–278), San Diego, USA. Qi, H., Iyengar, S. S., & Chakrabarty, K. (2001). Multi-resolution data integration using mobile agents in distributed sensor networks. IEEE Transaction Systems, Man, and Cybernetics Part C: Applications and Review, 31(3), 383–391. Straser, E. G. & Kiremidjian, A. S. (1998). A modular, wireless damage monitoring system for structures. Technical Report 128, John A. Blume Earthquake Engineering Center, Stanford University, Stanford, CA. Wu, J., Yuan, S. F., Zhao, X., Yin, Y., & Ye, W. S. (2007). A wireless sensor network node designed for exploring a structural health monitoring application. Smart Materials and structures, 16(5), 1898–1906. Xu, N., Rangwala, S., Chintalapudi, K. K., Ganesan, D., Broad, A., Govindan, R., et al. (2004). A wireless sensor network for structural monitoring. In Proceedings of the second international conference on embedded networked sensor systems. New York: ACM Press. pp. 13–24. Yuan, S. F., Lai, X. S., & Zhao, X., et al. (2006). Distributed structural health monitoring system based on smart wireless sensor and multi-agent technology. Smart Materials and Structures, 15(1), 1–8.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring Jian Wu a, Shenfang Yuan a,*, Sai Ji b, Genyuan Zhou c, Yang Wang a, Zilong Wang a a
The Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29# Yu Dao Street, Nanjing 210016, PR China Nanjing University of Information Science and Technology, 219# Ningliu Road, Nanjing 210044, PR China c Jinangsu Teachers University of Technology, 1801# Zhongwu Road, Changzhou 213001, PR China b
a r t i c l e
i n f o
Keywords: Multi-agent Wireless sensor network Structural health monitoring
a b s t r a c t Much attention has been focused on the research of structural health monitoring (SHM), since it could increase the safety and reduce the maintenance costs of engineering structures. In recent years, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based SHM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of six different agents for SHM applications, the distributed wireless sensor network can automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an experimental aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multi-agents software architecture is defined. The multi-agent monitoring principle and implementation in the validation work for two typical kinds of structure states are presented. This paper shows the efficiency of the multi-agent technology for WSN based the SHM applications on the large aircraft structures. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction The performance of the in-service aerospace structures can be affected by degradation resulting from exposure to harsh flight environment conditions or damages resulting from external conditions, such as impact, loading, operator abuse or neglect. In order to improve the safety level of the aerospace structures, structural health monitoring (SHM) is researched for devoted to predict the onset of damage and deterioration in aircraft structural condition with the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors. Traditionally, the cable-based SHM systems for aircraft structures might involve large number of wires employed for communication among sensors and centralized data acquisition systems. If the SHM system designed for a large structure is comprised of a large number of sensors and address stringent real-time operations, the system can become overburdened with computational tasks. In response to the cumbersome wires and performance shortcomings of centralized cable-based SHM systems, wireless sensor network (WSN) has been explored for adoption in recent years (Caffrey * Corresponding author. E-mail address: [email protected] (S. Yuan). 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.06.098
et al., 2004; Lynch et al., 2003; Straser & Kiremidjian, 1998; Xu et al., 2004). For SHM system applied on large scale structure, different kinds of density sensor networks are required to monitor different structure parameters, such as stress, stain, displacement, acoustic, pressure, temperature and etc. These sensors with different theories and functions might be connected to the wireless sensor nodes for data acquisition and processing. Many benefits can be gained from wireless SHM system, such as local computational ability, low cost deployment, and wireless networking functionality. However, the information obtained by each sensor node is limited, so does the local signal processing ability of each sensor node. Besides, real large scale aircraft structures are complicated to be estimated. Hence, when implementing a wireless SHM system for practical big aircraft structures, the challenge might includes how to integrate dedicated wireless SHM sensor nodes; how to coordinate and manage the large dense wireless SHM network, since the SHM systems for practical aircraft structures need multi-point, heterogeneous, and synchronous operations. Multi-agent technology over the past few years has come to be perceived as crucial technology not only for effectively exploiting the increasing availability of diverse, heterogeneous and distributed on-line information sources, but also as a framework for
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building large, complex and robust distributed information processing systems which exploit the efficiencies of organized behavior. Given the general benefits of multi-agents, scholars have explored the possibility for sensor network applications (Helvik & Wittner, 2001; Kumar, Shepherd, & Zhao, 2002; Qi, Iyengar, & Chakrabarty, 2001). A design method for MAS based SHM system has been presented by the authors (Yuan, Lai, & Zhao, 2006). This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. The collaborative multi-agent wireless sensor network architecture including the physical and software layers is presented. Six cooperation agents for SHM applications are defined based on the clustered wireless sensor network which could automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an aircraft aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multiagent software implementations are introduced in the validation work for two typical kinds of structure states. The evaluation system and experimental result shows the efficiency of the multiagent technology for WSN based SHM applications on large aircraft structures.
2. Collaborative MA-WSNs architecture based SHM To apply the multi-agent technology to the distributed WSN based SHM applications, a multi-agent architecture for sensor networks that can support collaborative SHM task allocation, network self-organization and sensor data aggregation is described from both physical and software architecture perspectives. 2.1. Physical architecture In SHM applications, groups of sensors are deployed in specific areas to observe quantities as stress, stain, acoustic or pressure and report to a remote central station in a multi-hop fashion. Sensors are typically grouped around specific points of interest in a component of the structure. From a network perspective, the nodes connected to these sensors might be deployed to clustered topologies, and although the size and the position of these clusters may vary significantly for different SHM applications, this similarity allows us to create protocols that can be effective over all these SHM applications. In the clusters, cluster heads can communicate with any other sensor nodes within its radio range to a star topology which might support synchronization simply. In the top layer, the station management node communicates with the cluster heads to a star topology. The clustered topology framework can completely support low-power, multi-point, and heterogeneous operations with a distributed mechanism. At the physical level, the sensor network consists of the following types of nodes: Sensor Nodes (SN): These are the smart network devices that communicate wirelessly and can be connected to sensors for structure monitoring. Based on the applications, any sensor node might act as the cluster head in their cluster. Cluster Heads (CH): These are the smart network devices that act as data aggregation and transmission, based on SHM application necessities. In the clusters, cluster heads can communicate with any other sensor nodes within its radio range to a star topology which might support synchronization simply. Station Management Node (SMN): The station management node is more powerful desktop workstation that has wireless links
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to the sensor nodes and communicates with the cluster heads to a cluster-star framework which can completely support low-power, multi-point, and heterogeneous operations with a distributed synchronization mechanism. They manage a network of sensor nodes, representing an application defined structure region. 2.2. Software architecture Architecturally, the sensor network software consists of two components, namely, sensor node (SN) software and station management nodes (SMN) software. The SN software contains a sensor manager that manages and measures the local sensor data. The SMN Software additionally provides data mining and management facilities over the regional sensor data and a code repository of useful task executables that can be ported to the sensor nodes on demand. The multi-agent system based SHM (Yuan et al., 2006) provides an information collaborating architecture for SHM application. This paper extends this model and defines an agent-based framework that can be useful for collaborative information processing in wireless sensor networks. The framework facilitates the integration of the hardware agents of the SHM application with the software agents. In this model, a sensor network application involves the following six types of software agents: 2.2.1. Structure monitoring agents (SMA) The SMA resides on a sensor node and has access to the monitoring data source of a sensor node in that region. It makes the sensor information of the represented sensor node available to the SHM application. In the architecture, there is a structure monitoring agents for every sensor node. In active SHM applications, the SMA might be act as the excitation signal provider. 2.2.2. Data manager agents (DMA) The main task of DMA is to manage and mine the regional sensor data. DMA can also be extended to support clustering, teaming, and routing. The structure monitoring agent registers itself with the data manager agent of the node on which it resides. 2.2.3. SHM application agents (SAA) The SAA is a mobile agent that is specific to the SHM application. It can possibly communicate, move, cooperate, reason, adapt, learn and perform other application specific tasks. It consists of four components, namely, ‘‘identification” to uniquely identify the mobile agent, ‘‘data space” to store intermediate integration results, ‘‘method” that hosts different integration algorithms, and ‘‘itinerary” that the mobile agent follows during migration. 2.2.4. Translation agents (TA) Its main task is to translate SHM application specifications into controllers for local nodes, such that the node behavior is consistent with the application-specific and platform-adaptive global network behavior. 2.2.5. Central collaboration agent (CCA) The CCA is implemented by software in the SMN to collaborate with DMAs in CHs for the whole monitoring process and fuse the different information from the different SAAs to obtain the most reliable and precise conclusion. It decides when to communicate with DMAs to dispatch and manage SAAs. 2.2.6. User interface agent (UIA) The UIA is implemented by the software in the SMN and the monitor of the computer system to accept commands from the user and show the monitoring results to the user.
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Of all the six types of agents, the sensor node may host SMA, DMA, TA and SAA, while the SMN mainly hosts SAA, CCA and UIA. Fig. 1 illustrates the multi-agent architecture for collaborative WSN based SHM applications and interrelationship among different agents. The design of this architecture is according to the following consideration. Considering a typical SHM system, the sensors are embedded in or bonded to the structure to sense the structural parameters. Appropriate signal or information processing methods are adopted to analyze and extract the features sensitive to the structural damage extracted from the sensed data. The local health status of the structure can be deduced using corresponding damage evaluation methods. Three kinds of agents are defined according to the three different functions. They are structure monitoring agents (SMA), data manager agents (DMA) and SHM application agents (SAA). The architectural framework supports agent mobility for SHM applications. Migration of a SAA from source to destination may trigger updates for the DMA and CCA agents. Among the four components of the SSA, the most important is the mobile agent’s itinerary. Itinerary can be determined either statically or dynamically. That is, it can be calculated either before the agent is dispatched or while the agent is migrating. Dynamic itinerary planning is more flexible, and can adapt to sensor data changing in real time. A large scale structure can be divided into some subsystems to be monitored and analyzed. When user is interested some region of the monitored structure, statically itinerary planning for SAA is created and decided by the interested position of the structure component. Static and dynamical itinerary planning in our architecture will be validated in the next section.
monitored by sensor arrays and indicate structural damages are researched such as joint failure and strain distribution change. 3.1. Evaluation system setup The setup of the practical system is shown in Fig. 2. The structure is a 2 m 1.2 m aviation hard aluminum (the type is LY12) plate with 2.5 mm thick, fastened to a steel frame by 64 bolts. The bolts are deployed around the frame with a distance of 100 mm. Except for the edge area with 100 mm 110 mm area to arrange the bolts, the whole structure is divided averagely into eight substructures with the dimension of 450 mm 490 mm. Each substructure is divided into 9 sub-areas which are all embedded four strain gauges. The dimension of every sub-area is 150 mm 140 mm. 12 piezoelectric ceramic sensors with 10 mm diameter are fixed on the corners of each substructure. Based on the designed multi-agent wireless sensor network architecture, this evaluation system should demonstrate following functions: the sensor network can automatically choose sensor nodes, selforganize wireless sensor network clusters, integrate suitable sensor data to localize the joint failure around the frame, and the static load on the structure which cause the strain distribution change in the structure. To have a better show of the advantages of the multiagent for wireless sensor network collaboration in SHM applications, two monitoring cases including monitoring the static load happened in the substructures by mobile agent SAA migrations and cycle active monitoring the joint failure avoiding pattern overlapping are demonstrated.
3. Multi-agent WSN architecture evaluation for SHM
3.2. Wireless sensor network platform development for the evaluation system
In order to validate the efficiency of the multi-agent WSN architecture for SHM, an aircraft plate structure as the typical engineering structure is adopted. Two typical structure states which may be
Because the performance of the entire wireless SHM system is dependent upon the individual wireless sensor nodes, one of the fundamental studies of the evaluation system is to design the
Sensor Nodes (SNs)
Interest Regions in SHM Stations Management Node (SMN)
SMA
DMA User Interface Agent
TA
SAA Cluster Heads (CHs)
Central Collaboration Agent
DMA TA Sensor Nodes (SNs) SMA
DMA
TA
SAA
Sensor Nodes (SNs)
DMA TA
Cluster Heads (CHs) Sensor Nodes (SNs)
SMA: Structure Monitoring Agents DMA: Data Manager Agents
SMA
SMA
SAA
SAA
SAA: SHM Application Agents DMA
TA
TA: Translation Agents : The migration of SAA
Fig. 1. The multi-agent architecture for collaborative WSN based SHM.
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Fig. 2. The picture of the evaluation structure and system setup.
Fig. 3. The schematic circuit diagram for conditioning the strain gauge sensor signal.
dedicated wireless sensor nodes for structure sensors connection. PZT sensors and strain gauges are typical structure sensors in SHM applications. In this paper, two dedicated sensor nodes are designed for strain distribution changes and joint failure monitoring. Based on the generalized wireless sensor node architecture (Hill, 2003), the design of the wireless sensor node is divided into three functional modules: sensor input unit, processing core, and wireless communication. Detailed design information on the processing core and wireless communication is outlined in our previous paper (Wu, Yuan, Zhao, Yin, & Ye, 2007). Specifically in the processing core design of this paper, the TI MSP430F1611 MCU is chosen instead of MEGA128L. Specifically in the wireless communication design, the TI CC2420 RF transceiver is chosen instead of CC1000. CC2420 is a true single-chip 2.4 GHz IEEE 802.15.4 compliant RF transceiver designed for low-power and low-voltage wireless applications. With 2.4 GHz RF frequency and 25 dB m output power, the wireless transceiver only draws 8.5 mA of current while actively transmitting, guaranteeing the low-power character of the designed wireless node. 3.2.1. A Wireless sensor nodes design for strain measurement Considering high-precision strain distribution monitoring requirement, the strain gauge signal conditioning circuit is designed for the sensor input unit of wireless strain nodes. The circuit is composed of three parts, the bridge circuit, the amplifier and the output circuit. The conditioning circuit provides a 3 V high-precision power supply for the bridge circuit. The bridge circuit output
Fig. 4. (a) The circuit design schematic for a sinusoidal signals actuator circuit; and (b) the circuit design schematic for PZT signals conditioning.
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Fig. 5. The picture of the designed wireless strain node, PZT node, station node and shock-proof encapsulation.
corresponds to structural strain monitored. Since the sensitivity of strain gauge is low, the instrumentation amplifier AD623 is adopted to amplifier the bridge circuit output. As a low-power instrumentation amplifier, the AD623 can offer excellent accuracy for the sensor input unit of the wireless sensor nodes. The strain gauges are usually adopted to monitor static signal, thus a lowpass filter is designed to eliminate the high frequency noise. The voltage follower is adopted to output the filtered voltage signal. Fig. 3 shows the schematic circuit diagram for conditioning the strain gauge sensor signal. Another hardware component of the strain sensor input unit is an analog-to-digital (A/D) converter that is chosen to accommodate external sensors with analog outputs. With greater than 200 ksps maximum conversion rate, the 12-bit analog-to-digital conversion integrated in MSP430F1611 MCU has eight individually configurable external input channels. 3.2.2. B Wireless sensor nodes design for joint failure active monitoring The joint failure monitoring is based on the structural vibration response of PZT sensors which can act both as the actuator or the
sensor. Based on this principle, the sinusoidal signal actuating and conditioning amplifier circuit for PZT sensors are designed. Fig. 4a shows the circuit design schematic for a sinusoidal signals actuator circuit, and Fig. 4b shows the circuit design schematic for a PZT signal conditioning circuit in the evaluation system. It can generate the 10 V peak-to-peak sinusoidal signal with the center frequency tunable from 0 to 120 KHz. Care is taken to select timing circuitry with very low standby current and fast start-up to meet the timing and limited power budget requirements. The design uses the timer TCL555: a low-power timer for the 0.02 ms pulse period. The resonant frequency is chosen near the operating frequency as a compromise between circulating current losses and sensitivity to variations in the effective load capacitance. A low-power amplifier chip OPA340 is adopted in the PZT signal amplifier circuit. Since the ADC module of MSP430F1611 can collect the analog signal from 0 V to Vcc which is supplied for 3 V, the output voltage amplitude of the voltage amplifier should at the range from 0 to 3 V. The signals can be converted right voltage by the addition circuit, and then be magnified through a two-stage amplifier circuit. Its magnification times are tunable from 50 to 100. Between the two-stage amplifier-circuits, there is a low-pass filter circuit in every series which can get rid of the high-frequency interference. 3.2.3. C Wireless station nodes design for the evaluation system This paper uses a USB controller IC from FTDI company to design the wireless station node for communicate with the host computer. Multiple wireless station nodes may be connected to a single computer’s USB ports at the same time. Each station node will receive a different COM port identifier. In the evaluation system, one station node is connected. UIA may read data and send instructions by the opening the COM port assigned to the station node which communicates with the host PC through USART1 on the MCU. After the design and IC choice of the circuit components, the 3 four-layer printed circuit boards for the three modularities and the dedicated installation box are designed and fabricated respectively. The reason to adopt the four-layer circuits and divided design is to sufficiently separate analog and digital circuit components. The other benefit gained from the design is that each part can easily be upgraded as advanced IC technologies and
Fig. 6. The sensor network deployed for the collaborative static load localization.
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different applications requirements. Fig. 5 shows the picture of the designed wireless strain node, PZT node and shock-proof encapsulation for lightweight installation. The power to supply the wire-
less sensor node is designed to use 3 V direct current (DC) power since all the components are low-power. Thus, two normal 1.5 V batteries can power the complete wireless sensor node.
CCA
DMA Monitoring time interval arrived? Periodic triggered tasks
New SAAs is added? User triggered tasks
Y
Y
SMAs of 4 sensor nodes collect
SAAs migrate toward to the
and store the strain variation data
destination CHs
DMA
Any Strain change >=20 με ? N Y DMA create a SAA
SAA complete migration in the cluster and integrate all strain data to its data space in the CH
CCA aggregate data from SAAs
UIA show the monitoring result Fig. 7. The workflow of the multi-agent implementation for static load localization.
Fig. 8. The sensor network deployed for cycle active monitoring of bolt loosening.
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3.3. Multi-agent implementation for collaborative static load localization The static load localization is based on the strain distribution variation monitored by the wireless strain sensor network nodes. As shown in Fig. 6, the sensor network is deployed by 16 sensor nodes, respectively connected to 16 strain gauges in four same dimension substructures I, II, III and IV. In this section, six types of software agents are implemented for the collaborative static load localization in the above-mentioned strain sensor network. Structure Monitoring Agents (SMA) is designed to access to the monitoring strain data source of its own sensor node. It makes the strain sensor information of the represented sensor node available
to the collaborative static load localization. Data Manager Agents (DMA) has two main tasks in the evaluation system. One is to decide when start to attach itself to the sensor nodes clustering. The other task is to decide the generation and migration of the SHM Application Agents (SAA). In the collaborative static load localization of the evaluation system, SHM Application Agents (SAA) act as a mobile agent that is specific to static load localization tasks. Their identifications are made up of the sensor node ID which monitor the strain data change and start the localization task. Their data spaces are used for storing intermediate data integration results. The methods host different pattern matching algorithms for load localizations. The itineraries save the next routing destination of SAA. Translation Agents (TA) is designed to the TinyOS function
DMA Monitoring time interval arrived? Periodic triggered tasks
CCA New SAAs is added? User triggered tasks
Y
Y
SMA of the i sensor node actuate the
The SAA itinerary is updated by the
connected PZT (i = 1 to 12)
destination nodes ID, including j, k ... z (1≤ j, k ... z ≤ the maximal nodes ID).
SMAs of the two sideward sensor nodes collect the peak values of the corresponding PZT signal.
DMA Any peak value change >=0.2 V ?
N
Y If there is no SAA, DMA of the i sensor node create a SAA
SAA integrate peak values of two adjacent nodes to its data space in the i sensor node; If SAA is desiccated to Periodic triggered task, “i = i +1” will be used for updating the itinerary of SAA.
Y
SAA i the maximal nodes number +1 ? orSAA do not complete the migration ?
N CCA aggregate data from SAAs
UIA show the monitoring result Fig. 9. The workflow of the multi-agent implementation for cycle active monitoring of bolt loosening.
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for strain data collection and processing by controllers of local nodes. Central Collaboration agent (CCA) and User interface agent (UIA) are implemented by Delphi development platform in the SMN to collaborate, monitor and control the whole monitoring process. In this paper, a loading equipment is used to change the strain distribution in the plate. 70N is chosen in the demonstration. When the concentrated load is applied on the structure or the applied position changes, the strain distribution changes correspondingly and the output mode of the strain sensor nodes changes too. The SAA classifies the different pattern data collected by SMA to decide the different load position. The pattern recognition method that the SAA adopted is the minimum-distance classification. The distance between two patterns is calculated using the Euclidean distance, shown in Eq. (1):
" dðx; yÞ ¼
n X
Table 1 Strain data from SN9 to SN12 in the substructure III (unit:
le).
Loading locallization SN
No load
1
2
3
4
5
6
7
8
9
9 10 11 12
0 0 0 0
18 269 37 8
18 36 46 45
4 23 25 320
25 0 39 10
32 3 50 4
16 8 45 9
272 25 106 68
80 7 121 35
33 8 324 31
#1=2 2
bxi yi c
ð1Þ
i¼1
where x; y indicate two different patterns, xi refers to the strain monitored by each SAA and yi refers to the reference strain stored in the data base related to different load position. Fig. 7 shows the workflow of the multi-agent implementation process for static load localization. The multi-agent implementation process for collaborative static load localization includes following steps. With a monitoring time interval, 4 SMAs of 4 sensor nodes in each substructure collect and store the strain variation data synchronously. Then if any strain variation exceeds 20le, DMAs creates a SAA and select a CHs in the 4 sensor nodes according to the residual energy. The threshold 20le is set according to the finite element modeling analysis based on MSC. Patran/ Nastran software and practical loading experiments. SAA will complete all strain data integration to its data space in the CH of the substructure. Finally the CCA and UIA will aggregate all triggered SAAs from CHs and show the integrated result about the structure situation. Besides the periodic monitoring, the users might want to know the structure state at any moment. In these cases, SAAs may be added by the CCAs and distributed to the CHs in the objective structure. Then the SAAs will act as the cooperation work like the periodic monitoring cases. 3.4. Multi-agent implementation for joint failure active monitoring The bolt loosening monitoring is based on the structural vibration response. Considering numerous structure joint bolts and existent pattern overlapping, cycle active monitoring method is designed for multi-agent wireless sensor network cooperation in the paper. In Fig. 8, the sensor nodes connected by PZT sensors around the boundary might act as actuator and CH in turn. Each time the actuator and the two sideward sensor nodes (left and right) form a cluster used for exactly monitoring adjacent bolts loosening. For example, as shown in Fig. 8, while SN 1 acts as the actuator, SN2 and SN6 will work as the sensor nodes. In this case, DMA in SN1 will create a SAA for integrating the structural vibration data of SN2 and SN6. In turn, while SN12 acts as the actuator, the SAA will migrate to SN12 and keep on aggregating the structural vibration data of SN1 and SN11 to its data space. Fig. 9 shows the workflow of the multi-agent implementation process for cycle active monitoring of bolt loosening. The multiagent implementation process for joint failure active monitoring includes following steps. When the periodic triggered tasks is allocated for joint failure monitoring, SMA of the sensor nodes which ID is 1 will periodically actuate the PZT and make the SMAs of the two adjacent nodes collect the peak values of the corresponding PZT signal synchronously. In this case, dynamical itinerary plan-
Fig. 10. The interface of the UIA implementation.
ning is designed for SAA migration. Then if any peak value exceeds 0.2 V, DMAs will create a SAA and integrate peak values of two adjacent nodes to its data space in the i sensor node. The threshold 0.2 V is set according to the finite practical bolt loosening experiments. Then ‘‘i = i + 1” will be used for updating the itinerary of SAA. SAA will complete all PZT data integration to its data space from SN1 to SN2 in the evaluation system. Finally the CCA and UIA will aggregate all PZT feature data and show the integrated result about the joint failure situation. If the users want to know the partial blots loosening states at any moment, a SAA added by the CCA might update its itinerary by the destination nodes ID, including j, k, . . . , z ð1 6 j; k; . . . ; z 6 the maximal nodes IDÞ. In this case, static itinerary planning is designed for SAA migration. Then SAAs for user triggered tasks will act as the same cooperation work during its migration like the periodic triggered tasks. 3.5. Evaluation experiment results Fig. 10 show the designed interface for UIA implementations. For the wireless strain node, the relationship between the output of the voltage change DVand the strain parameter variation De is as following: DV ¼ K4 De V. K is the sensitivity coefficient of the strain gauge, K ¼ 2. V is the bridge voltage provided, V ¼ 3v : Table 1 shows the strain data from SN9 to SN12 in the substructure III stored in the UIA. These strain data could be used for the trained data to decision making of static load localization by CCA. Table 2 lists a part of the monitoring result when bolt 1, 2, 3 is loosening, respectively. 4. Conclusions The evaluation work shows that multi-agent WSN based SHM could gain the following advantages. The collaborative multi-agent
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Table 2 The Euclidean distance of different modes. Mode
0
1
2
3
4
5
6
7
8
9
10
1 2 3
2.36 2.01 2.31
1.03 3.18 2.20
2.54 0.74 2.21
1.35 1.98 1.28
2.15 3.18 2.70
1.43 2.61 2.08
1.91 2.67 2.21
1.86 1.58 2.25
1.75 3.24 2.35
2.54 2.56 2.45
2.33 2.58 2.15
wireless sensor network architecture could automatically allocate SHM tasks by the mobile agent (SAA). According to different SHM task trigger mode, such as periodic triggered tasks and user triggered tasks, static and dynamical itinerary planning might be used for the design of the itinerary of SAA. Clustered sensor network can be changed and self-organized by SAA during its migration. The distributed data integration of SAA could help to decrease the amount of packages transmission and energy dissipation. By adding the suitable threshold in SAA implementation, the sensor performance shifting and the influence from environment parameters to the sensor can be eliminated. Acknowledgements This work is supported by Natural Science Foundation of China (Grant Nos. 60772072 and 50830201), National High-Tech Research and Development Plan (863 Plan). (Grant No. 2007AA03Z117). References Caffrey, J., Govindan, R., Johnson, E., Krishnamachari, B., Masri, S., & Sukhatme, G., et al. (2004). Networked sensing for structural health monitoring. In Proceedings of the fourth international workshop on structural control (pp. 57–66), June 10–11, New York, NY.
Helvik, B. E., & Wittner, O. (2001). Using the cross-entropy method to guide/govern mobile agent’s path finding in networks. In Proceedings of third international workshop on mobile agents for telecommunication applications, 2001 (pp. 255– 268). Hill, J., (2003). System architecture for wireless sensor networks. Ph.D. thesis, UC Berkeley. Kumar, S., Shepherd, D., & Zhao, F. (2002). Collaborative signal and information processing in micro-sensor networks. IEEE Signal Processing Magazine, 19(2), 13–14. Lynch, J. P., Sundararajan, A., Law, K., Kiremidjian, A., Carryer, E., & Sohn, H. (2003). Field validation of a wireless structural monitoring system on the Alamosa Canyon Bridge. In Smart structures and materials 2003: Smart systems and nondestructive evaluation for civil infrastructures spie’s 10th annual international symposium on smart structures and materials (Vol. 5057, pp 267–278), San Diego, USA. Qi, H., Iyengar, S. S., & Chakrabarty, K. (2001). Multi-resolution data integration using mobile agents in distributed sensor networks. IEEE Transaction Systems, Man, and Cybernetics Part C: Applications and Review, 31(3), 383–391. Straser, E. G. & Kiremidjian, A. S. (1998). A modular, wireless damage monitoring system for structures. Technical Report 128, John A. Blume Earthquake Engineering Center, Stanford University, Stanford, CA. Wu, J., Yuan, S. F., Zhao, X., Yin, Y., & Ye, W. S. (2007). A wireless sensor network node designed for exploring a structural health monitoring application. Smart Materials and structures, 16(5), 1898–1906. Xu, N., Rangwala, S., Chintalapudi, K. K., Ganesan, D., Broad, A., Govindan, R., et al. (2004). A wireless sensor network for structural monitoring. In Proceedings of the second international conference on embedded networked sensor systems. New York: ACM Press. pp. 13–24. Yuan, S. F., Lai, X. S., & Zhao, X., et al. (2006). Distributed structural health monitoring system based on smart wireless sensor and multi-agent technology. Smart Materials and Structures, 15(1), 1–8.