Structural Health Monitoring System Using

Wireless Sensor Network

Kavita KumariStudent, Dept. of IT

UIET,PU, Chandigarh Email: kavita06it16@gmail.com

Inderdeep Kaur AulakhAsst. Professor, Dept.Of IT

UIET, PU, ChandigarhEmail: ikaulakh@yahoo.com

Amol P BhondekarPrincipal Scientst

Agrionics,CSIO, ChandigarhEmail:amol.bhondekar@gmail.com

Abstract— The longevity and health monitoring of structure areimportant for their lifespan optimization and preservation. WSNtechnology has proven to be a boon for structural health monitor-ing in recent year due to its ease of installation, minimal struc-tural intervention/damage and low cost. This paper provides a re-view on the recent developments in the area of SHM using WSNs.

Keywords: wireless sensor network; structural health monitoring;scheduling approach; energy efficiency

I. INTRODUCTION

Structural Health Monitoring (SHM) is referred as the processof implementing damage detection and characterizationstrategy for engineering structures. The changes to thematerial and/or geometric properties of a structural system,including changes to the boundary conditions and systemconnectivity which adversely affect the system’s performance,is defined as damage. In SHM process we observe systemusing periodically sampled dynamic response measurementsfrom an array of sensors. Then the extraction of damage,damage-sensitive features from these measurements arecarried out. To determine the current state of system health,the statistical analysis of the features is performed. There will be inevitable aging and degradation in the structureresulting from operational environment. Long term SHM isdefined as output of this process that is periodically updatedregarding the ability of the structure to perform its intendedfunction. Regarding the integrity of the structure, SHM is usedfor rapid condition screening and to provide near real timereliable information, for example in case of extreme eventssuch as earthquakes or blast loading [1]. To estimate the stateof structure health, SHM detects the changes in structure thateffects its performance. Time- scale of change and severity ofchange are two major factors. How quickly the change occursis time- scale of change, and degree of change is severity ofchange. SHM has two major categories: disaster response(earthquake, explosion, etc.) and continuous health monitoring(ambient vibration, etc.). SHM has two approaches: directdamage detection (visual inspection, and X- ray, etc) andindirect damage detection (change in structuralproperties/behavior). A typical SHM system, in general,includes three major categories: a sensor system, a data

processing system (including data acquisition, transmission,and storage), and health evaluation system(includingdiagnostic algorithms and information managements).

II. IMPORTANCE OF STRUCTURAL HEALTH

MONITORING

There is a significant development in SHM due to majorconstruction projects, such as large dams, long- span cablesupported bridges and offshore gas/oil production installation.SHM infrastructure provides the means for society to function.It also includes buildings, pedestrian and vehicular bridges,tunnels, factories, conventional and nuclear power plants,offshore petroleum installations and heritage structures.

A. BridgesFor the purpose of understanding and eventually calibratingmodels of the load-structure-response chain, bridgemonitoring programmes have historically been implemented.

B. Buildings and towersThe need to understand building performance duringearthquakes and storms, the developments in monitoring ofbuildings werehistorically motivated. Originally, fromvibration testing, the understanding of low-amplitude dynamicresponse was obtained. [3]

C. Nuclear installationsFor one of the UK's civil nuclear reactors, Smith (1996)provided an overview of the inspection and monitoringregime. To validate and calibrate designs during performancetesting, the safety- critical structural components of nuclearreactors, instrumentation were used. It also contributed to thecondition monitoring during normal operation. [4]

D. Tunnels and excavationsIn terms of stability and effects on or from adjacent structures,tunnel monitoring is aimed to ensure whether tunneldeformation is within limits. Hence, the emphasis is ondeflections, while stresses and strains may also be measured.

During tunneling or mining, monitoring of heritage and otherstructures is a major concern. [5]

III WIRELESS SENSOR NETWORKS (WSNS)

The development of Wireless Sensor Network(WSNs) hasoriginated from the need to continuously monitoring thephysical phenomena coupled with the recent advances insensing, computing and communication technologies. WSNconsist of four main components: sensors, a processor, aradio, and a battery. In an application area, the WSN is formedthrough densely deployed sensors nodes. To collaborativelyperform a particular task, in most deployments the sensornodes have self- organizing capabilities to form an appropriatestructure. WSNs are found suitable for applications such assurveillance, precision agriculture, smart homes, automation,vehicular traffic managements, habitat monitoring, anddisaster detection. To revolutionize information andcommunication technology, WSN has great enablingtechnology. WSN connects the physical world to the Internetat fine granularity.WSN has power of creating a pervasiveenvironment capable of remote sensing, monitoring andcontrol. As a benefit, this technology offers fine granulartracking of actions in far away or inaccessible locations. WSNcan also enable remote monitoring of components responsiblefor global warming.

IV. SENSORS FOR SHM

The sensing system in the SHM is formed by smartmaterials/sensors;Fibre optic sensors (FOS), piezoelectricsensors, magnetoresistive sensors, and self - diagnosing fibrereinforced structural composites. Thesesensorsarecharacterized with very important capabilities of sensingvarious physical and chemical parameters related to the healthof the structures.

A.FIBRE OPTIC SENSORS (FOSS)

FOS can be classified by several methods. FOS can beclassified based on the modulation of light characteristics(intensity, wavelength, phase, or polarization etc.) by theparameters to be sensed. It can also be classified by themethodthrough which the light in the sensing segments ismodified inside or outside the fibre (intrinsic or extrinsic).FOS can also be classified based on the sensing range; local(Fabry- Perot FOS or long - gauge FOS etc.), quasi-distributed(fibre Bragg grating) and distributed sensors(Brillouin-scattering-based distributed FOS). FOS areembedded in newly constructed civil structures, includingbridges, buildings, and dams to yield information about strain( static and dynamic), temperature, defects (delamination,cracks, and corrosion ) and concentration of chloride ions. Onexisting structures, FOSsare generally surface mounted. Thedata collected by FOSs is used to evaluate the safety of boththe new-built structures and repaired structures, and diagnosethe location and degree of damage.

B. Piezoelectric SensorsPiezoelectric materials exhibit simultaneous actuator/sensorbehavior based on electrical-mechanical transformation. Thereare many types of piezoelectric materials: piezoelectricceramics, piezoelectric polymers, and piezoelectriccomposites. Based on the measurement of electricalimpedance and elastic waves piezoelectric sensors wererecently introduced into SHM of civil engineering structuresas an active sensing technology.

C. Magnetostrictive SensorsFerromagnetic materials are the materials which aremechanically deformed when placed in magnetic field. Thisphenomenon is known as the magnetostrictive effect. Intheinverse magnetostrictive effect, the magnetic induction of thematerial changes when the material is mechanicallydeformed.Based on the above phenomena, Kwun and Bartels[10] invented a type of magnetostrictive sensor (MsS) withoutdirect physical contact to the material surface which couldgenerate and detect guided waves in the ferromagneticmaterials under testing. Khazem et al. [11] also utilized MsSto inspect suspender ropes on the George Washington Bridgein New York. A pulse of 10 kHz longitudinal guided wavealong the length of the suspenderdetected the reflected signalsfrom geometric features and defects in the suspender.

V. ROUTING ALGORITHMS FOR WIRELESS SENSOR

NETWORKS

A. Data-centric protocolsDue to the sheer number of nodes deployed, it is not feasibleto assign global identifiers to each node in many applicationsof sensor networks. It is hard to select a specific set of sensornodes to be queried due to lack of global identification andrandom deployment of sensor. Therefore, from every sensornode, the data is usually transmittedwithin the deploymentregion with significant redundancy. Since this is veryinefficient in terms of energy consumption, routing protocolshave been considered that select a set of sensor nodes andutilize data aggregation during the relaying of data. Thisconsideration is known as data-centric routing. In data-centricrouting, the sink sends queries to certain regionsand waits fordata from the sensors located in the selected regions.Attribute-based naming is necessary to specify the propertiesof data, since the data is being requested through queries. Inthe first data-centric protocol SPIN [14],data negotiationbetween nodes is considered in order to eliminate redundantdata and save energy. Later, a breakthrough Directed Diffusion[18] data-centric routing has been developed. Based onDirected Diffusion [17–18], many similar concepts andprotocolshave been proposed [16,15,19,20].

B. Flooding and gossipingTo relay data insensor networks without the need for anyrouting algorithms and topology maintenance, there are two classical mechanisms: : Flooding and gossiping [21].Inflooding, each sensor receives a data packet, broadcasts it to

all of its neighbors and this processcontinues until the packetarrives at the destination or the maximum number of hops forthe packetis reached. Gossiping is a slightly enhanced versionof flooding, where thereceiving node sends the packet to arandomly selected neighbor, and that neighbor picks anotherrandomneighbor to forward the packet to and so on.

C. Sensor protocols for information via negotiationSPIN technique was amongst the early works to pursue adata-centric routing mechanism [15]. SPIN uses high-leveldescriptors or meta-data. A dataadvertisement mechanism isused to exchange the data among the sensorsbeforethetransmission which is the key feature of SPIN. Afterreceiving new data, each node advertises it to its neighborsand interested neighbors.

D. Directed DiffusionIn the data-centric routingresearch of sensor networks,Directed Diffusion [18,19] is an important milestone. The ideais to diffuse data through sensor nodes by using anamingscheme for the data.

E. Energy-aware routingTo increase the lifetime of the network, Shah and Rabaey [19]proposed the occasional use of set of sub-optimal paths.Depending on the energy consumption, these paths are chosenby means of a probability function. The approach is concernedwith network survivability as the main metric. Energy-awareroutingapproach argues that using the minimum energy pathall the time will deplete the energy of nodes on that path. Theassumption of the protocol that each node is addressablethrough a class-based addressing which includes thelocationand types of the nodes.

F. Rumor routingDirected Diffusion has another variation ‘Rumor routing’ [16].It is used in contextswhere geographic routing criteria are notapplicable. Generally, in the entire network, DirectedDiffusion floods the query when there is no geographiccriterion to diffuse tasks. The use of flooding is unnecessary insome cases where only a little amount of data is requestedfrom the nodes. When the number of queries is large and thenumber of events is small, an alternative approach floods theevents.

G. Gradient-based routingA slightly changed version of Directed Diffusion, calledGradient-based routing (GBR) was proposed by Schurgers etal. [17]. Here each node can discover the minimum number ofhops to the sink, which is called height of the node. Thegradient on the link is considered as the difference between anode'sheight and that of its neighbor on that link. A packet isforwarded on alink with the largest gradient.

H. CADRConstrained anisotropic diffusion routing (CADR) is ageneralform of Directed Diffusion [18]protocol. Two techniques are

proposed:information driven sensor querying(IDSQ) andconstrained anisotropic diffusion routing. The idea is to querysensorsand route data in a networkin order to maximize theinformation gain while minimizing the latency andbandwidth.This is achieved by activating only the sensors thatare close to a particular eventand dynamically adjusting dataroutes.

I. COUGARThe network as a huge distributed database system in data-centric protocol has beenproposed [14]. The main idea is touse declarative queries in order to abstract queryprocessingfrom the network layer functions such as selectionof relevant sensors etc. and utilize in-networkdata aggregationto save energy. The abstraction is supported through a newquery layer betweenthe network and application layers.

J. ACQUIREActive Queryforwarding in sensor networks (ACQUIRE) [20]is a fairly new data-centric mechanism for querying sensornetworks.The approach views the sensornetwork as adistributed database and is well-suited for complex querieswhich consist of severalsub queries.

TABLE 1 COMPARISON OF PROTOCOLS AND FEATURES

RoutingProtocol

Data-Centric

Hierarchical

Locationbased

QoS Network flow

Dataaggregatio

n

SPIN

DirectedDiffusion

Rumorrouting

Shah andRabey

GBR

CADR

COUGAR

ACQUIRE

LEACH

TEEN andAPTEEN

PEGASIS

MECN andSMECN

GAF

GEAR

Chang andTassiulas

SAR

SPEED

Fe et al.

VI. ISSUE RELATED TO WSN

Depending on the application scenario and specific structure,issues related to WSN for monitoring structural health systemsmay impose different requirements. The following issue arethe base of the building structure.

A. Quality of Data

Data is the essential evidence. Quality of data is moreimportant because it carries structural health information. Anymissing data is an error result of the analysis. Other parameterrelated to signal processing must be accurately specified duringsignal synchronization. To continue error free analysis, losslessdata transmission is required and packet/symbol/bit error mustbe avoided.

B. Reliability and Scalability

It seems that wireless communication could be unreliablebecause is uses a share transmission media and informationerror is also calculated on probability base. Increases in thetransmission node in the network lead collision and packet loss.Unknown errors and lack of reliability may also occur whileanalyzing the results.

One of the most important issues is to cover the largegeographical civil infrastructure. Scalability of the WSN willprovide adjustment flexibility with infrastructure formonitoring structural health by adding new transmission nodein the network with higher precision of damage detection. Thesensor coverage area defines the complexity of the scalabilityto cover the whole service area.

C. Real-time Response and Lifetime of the Overall

The measurement of the overall system should be a real timeresponse. Efficient design of the fault management solution ofthe wireless sensor network is an another important challengebased on real time environment. Every system is defined byreal time response. The faster real time system response mayprovide more accurate data and such system helps in correctdecision for better result.

The lifetime of the overall monitoring system should beincreased to reduce the overall system cost. Limitedmaintenance and power efficiency are also the importantparameters.

CONCLUSION

This paper presents a review of recent research anddevelopment activities in SHM of civil structures and discussesseveral techniques that evaluate structural damage and issue

related to the WSN. Traditionally, wired system is used forcollecting sensor data periodically, but the SHM system hasseveral disadvantages. The main issues in the use of WSN inSHM are the scalability, accuracy, reliability and dataprecision.

REFERENCES

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