a framework of intelligent sensor network with video camera for structural health monitoring of
TRANSCRIPT
A Framework of Intelligent Sensor Network with Video
Camera for Structural Health Monitoring of Bridges
Arslan Basharat*, Necati Catbas** and Mubarak Shah*
*Computer Vision Lab and **Civil Structures Lab,University of Central Florida
Orlando, FL
Outline
IntroductionStructural Health Monitoring (SHM)Framework Details
Network LayoutVibration SensingEvent Detection
Test-bed overview and demonstration VideoConclusion
Introduction
Wireless sensor networks have been used in various areasAdvances in Micro Electro-Mechanical Systems (MEMS) propose the use for civil structural health monitoring Higher data rate requirementsOur framework for this task augmented with video cameras
Structural Health Monitoring
Constantly monitor status of the structureDetect abnormal behaviorLocalize structural damageVarious SHM Systems
Visual Inspection by HumansTomography (Ultrasound, X-rays etc.)Vibration & Strain Monitoring...
Structural Health Monitoring
Essential characteristics Large number of sensorsSensor types
Vibration Tilt Strain
Data acquisition system for data recordingCentralized data interpretation
Structural Health Monitoring
Drawback of conventional wired systemExpensiveHuge mesh of cablesCentralized processing onlyLarger response timeLess Fault-Tolerant
Structural Health Monitoring
Advantages of using a wireless systemInexpensive Wireless data communicationDistributed processing possible
Improved response timeNear real-time performance
More fault-tolerant systemRedundancyModularity
Scalability
SHM of Bridges
Need of visual surveillanceTraffic monitoring Activity detection
Autonomous correlation betweenVideoOther sensor data (e.g. vibration, strain)
Intelligent sensing and actuation
SHM of BridgeAn example of wired SHM system
Drexel University / Commodore Barry Bridge
Proposed Framework
Wireless sensor nodesCentral station
Data collectionVideo cameras controller
Web-Server
Mote
Wireless Internet
Sensor Network
Mica motesMTS series sensor boardsSensors used
AccelerometerTemperature
Stationary video cameraPan/tilt/zoom featureControlled through central station
Network Architecture
Clusters based networkCluster head
Gateway node
Cluster member2-5 Sensing nodeBackup nodes (Gateway, Sensing)
Multi-hop to base station through cluster heads
Sensing node
Sensing node
Sensing node
Gateway node
Backup node
Backup node
Node Layout on Bridge
Sensing Node Backup Node Central Base Station
Data pathClusterGateway Node
Neighbor Discovery
Central station to gateway nodeGateway node to gateway node progressive listGateway node to sensing node progressive list
Sensing node
Sensing node
Gateway node
Sensing node
Sensing node
Gateway node
Sensing node
Sensing node
Gateway node
Vibration Sensing
Vibration data from Accelerometer2-axis of vibrationInduced vibration through impact hammerResponse from lab model of steel bridgeSensor with Mica2 mote
Vibration Sensing
Sampling frequency requirements100Hz-200HzHigher data rateSmaller network battery lifee.g. 6 nodes @ 150Hz x 2 axis~ 216 KB/min
Solution: Adaptive Sampling
Adaptive SamplingExploit silent zonesTransmit only useful dataModes of Sensing Nodes
Sleep modePassive sensing
Low sampling frequency ~80HzOnly two sampling nodes/cluster
Active sensingHigher sampling frequency ~ 150HzLimited time period (45 secs)Stored in Flash memoryTransferred through cluster head in allocated time slice
Adaptive Sampling
1 passive/rest sleepHigh vibration event detectedAll activeActive for limited timeRound-robin passive sensingHow to detect events?
Sensing Node
(Sleep)
Sensing Node
(Sleep)
Sensing Node
(Passive)
Gateway Node
Sensing Node
(Sleep)
Sensing Node
(Passive)
Sensing Node
(Sleep)
Gateway Node
Sensing Node
(Active)
Sensing Node
(Active)
Sensing Node
(Active)
Gateway Node
Event Detection
Vibration EventsActivity metric on 1-D vibration dataA good solution
Thresholded Mean Shift Vector
Mean Shift Vector
Always points towards the direction of maximum increase in densityOnline model: Can be applied in an iterative fashion
where,nx is the number of pointsyo is the last mean valuexi is ith sample
Interesting Events
SENSE_EVENTSuppression of unnecessary dataLower mean shift threshold TL
Change sensing mode (Passive/Active)CAMERA_EVENT
Critical event notification for visual inspectionHigher mean shift threshold TH
Trigger video camera request in cluster
Event Detection
SENSE_EVENTMean shift threshold TL exceededTrue positive
CAMERA_EVENT detected
Mean shift threshold TH exceededTrue positive
Camera Events
Gateway Nodes
Camera Event
Stationary wireless sensor nodes Controlled gateway node deploymentStationary pan/tilt/zoom video camera Central station attached to both video camera and WSNRegistered gateway nodes with approximate 1-D distance
Node Registration
Utilizing the gateway node-gateway node progressive listRegistering approx. gateway node positionsFOV wide enough to cover the cluster
Sensing node
Sensing node
Gateway node
Sensing node
Sensing node
Gateway node
Sensing node
Sensing node
Gateway node
Optimizations
Single time-stamp and current sampling frequency transmitted for the whole data packet Mean-shift vector test after 45secs of active samplingData packing for transmission saves ~38% of unused buffer
Test-bed Setup
Test-bed Setup
Demo Video
Overview of the test-bedVibration induced by impact hammerEvent detection by sensing nodes Camera motion
Conclusion
Wireless sensor network have a promising application in the area of SHMDistributed processing enhances the system effectivenessDomain specific knowledge for data compressionTested in controlled lab environment, effectiveness of the system remains to be seen in real-life situation