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Page 1: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

A Programmable Sensor Network A Programmable Sensor Network Based Structural Health Monitoring Based Structural Health Monitoring

SystemSystem

Krishna Kant Chintalapudi

Embedded Networking Laboratory,

University of Southern California, Los Angeles, USA

Page 2: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for sensor network based SHM

• A prototype – implementation and deployment

• What next?

Page 3: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What’s the talk about?What’s the talk about?

• A programmable sensor network based system for structural health monitoring

• What are the requirements of SHM applications?

• How do we architect a sensor network system to satisfy these requirements?

• A prototype and its performance

Page 4: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for sensor network based SHM

• A prototype – implementation and deployment

• What next?

Page 5: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What Is Structural Health Monitoring What Is Structural Health Monitoring (SHM)?(SHM)?

• Structural integrity assessment for buildings, bridges, offshore rigs, vehicles, aerospace structures etc.

• Goals of SHM are:

– damage detection “is there damage?”

– damage localization “where is the damage?”

– damage quantification “how severe?”

– damage prognosis “future prediction”

Page 6: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

How Are Damages Caused?How Are Damages Caused?

• Extreme stress leading to fatigue in elements

– several freeway bridges today bear traffic far exceeding tolerance levels they were originally designed to bear.

• Rusting and degradation of material properties

– leads to change in stress distribution and overloading of certain elements more than others

• Continuous vibrations/cyclic stresses in the structure

– waves shaking offshore oil-rigs, gales shaking bridges.

• Catastrophes (earthquakes)

Page 7: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

How Do Damages Evolve?How Do Damages Evolve?

• Most damages start as tiny cracks caused by metal fatigue (microns-mm).

• If unattended the cracks creep and grow in size leading to deterioration of the material.

• If unchecked, it eventually results in an unpredictable, sudden and catastrophic failure.• SHM techniques focus on detection and localization of damages as early as possible.

Page 8: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

SHM TodaySHM Today•Today SHM is carried out by

– collecting sensor data from several locations in the structure and analyzing it on a high end platform

– periodic (bi-annual) human inspections (visual/using portable devices),

– expensive and dedicated data-acquisition systems (for structures where monitoring is critical) .

• SHM suffers from

– human error and inaccessibility of locations within the structure

– expensive labor (for inspection), cabling and installation (for data-acquisition systems)

– possibility of catastrophic failure between inspections

Page 9: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for sensor network based SHM

• A prototype – implementation and deployment

• What next?

Page 10: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Local vs. Global TechniquesLocal vs. Global Techniques

• Use sophisticated imaging techniques – 250KHz ultrasound, x-ray, thermal, magnetic etc.

• Use accelerometers to collect structural response.

LOCAL GLOBAL

• Detect tiny cracks (mm/cm) and small corroded patches.

• Target larger damages e.g. undermined cables, braces or columns

• Can detect damages within a few inches of the equipment

• Detect structural damages in the entire structure

Page 11: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Feasibility of Local SHM Feasibility of Local SHM Techniques Techniques

• They are expensive, require a lot of power and bulky

• Demand extremely dense deployments

• Local SHM techniques are not amenable to sensor network deployments

• So let us focus on global schemes henceforth

Page 12: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Ambient vs. Forced Excitation Ambient vs. Forced Excitation

• Low signal-to-noise ratio. • Much higher signal-to-noise ratio.

AMBIENT FORCED

• Rely on ambient sources (wind, passing vehicles, earthquakes)

• Rely on induced excitation (impact hammer, rotating mass etc.)

• Unpredictable in nature and timing

• Pre-meditated and precise.

• Require continuous monitoring; hard to implement duty cycles.

• Amenable to extremely low duty cycle functioning.

Page 13: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Recall Our Goal…Recall Our Goal…

• We want a system that SHM engineers can program … not experts in TinyOS

• We explore existing SHM schemes to find what SHM engineers want?

• We design our system based on requirements of SHM schemes.

Page 14: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What SHM Engineers want?What SHM Engineers want?

• Structural integrity assessment for buildings, bridges, offshore rigs, vehicles, aerospace structures etc.

• Today SHM engineers want:

– damage detection “is there damage?”

– damage localization “where is the damage?”

– damage quantification “how severe?”

– damage prognosis “future prediction”

Page 15: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Structural Dynamics 101Structural Dynamics 101

Structures are no different from strings!!

Page 16: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Structural Dynamics 101…Structural Dynamics 101…• Structural response is the spatio-temporal deformation induced in the structure.

• The dynamics of a structure are often expressed as,

• The impulse response is given by

• vl are mode shapes – normalized structural deformation patterns

• are modal/resonant frequencies of the structure

• are the amplitude and phase of the mode induced in the structure

)(''' tfKyyCyM

ml

lll

tll tevaty l

1

cos)(

l

lla ,

Page 17: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Structural Dynamics 101…Structural Dynamics 101…

• mode shapes and frequencies are fundamental to the structure

• material properties, geometry and assemblage of elements

• depend on both the sensing and actuating locations

• mode are global phenomena – may span the entire structure

ml

lll

tll tevaty l

1

cos)(

lla ,

Page 18: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

How Does Damage Affect Modes?How Does Damage Affect Modes?

• Modal (resonant) frequencies and mode shapes change

• Modal frequencies decrease

• Break in symmetry of the structure may lead to splitting of overlapping modes and cause extra modes to appear

• Non-linearities may introduce new modes.

Page 19: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Some practical aspectsSome practical aspects

• Modal frequencies are typically in the range of few tens of Hz

• Real structures are often heavily damped and decay within a second

• Most SHM engineers prefer 10 times oversampling

• Sampling rates desired are around 200-500Hz for most structures.

Page 20: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Literature Review – Damage DetectionLiterature Review – Damage Detection

• Model the structural response using ARMA/AR based linear predictors and look for a significant change in coefficients.

• Look for shifts/changes in modal frequencies through spectral analysis.

• Look for changes in mode shapes.

• Use non-linear techniques such as neural networks.

• Literature is very vast

Page 21: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Literature Review – Damage LocalizationLiterature Review – Damage Localization

• Significantly more challenging and still a very hot research topic.

.

• Time domain methods, model structure as a LTI system

• try to solve for A,B,C and D using response from all sensors

• compute stiffness of elements using A, B, C and D

• loss of stiffness indicates damage in an element

)()()(

)()()('

tDutCxty

tButAxtx

Page 22: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Damage Localization Techniques…Damage Localization Techniques…

• Frequency domain - estimate mode shapes using structural response from all sensors and use mode shapes to estimate stiffness of members

• ERA (Eigenvalue Realization Algorithm) – perform SVD on the Hankel matrix

y is the impulse response

vector

• Select modes corresponding to the high singular values to forma reduced order system, and calculate the modal vector matrix V using,

PEQH

rpkyrkyrky

pkykyky

pkykyky

kH

)0(,

)(...)1()(

............

)1(...)2()1(

)(...)1()(

)(

CV

IFVDPFC

EQHPEA

pTpnn

Tn

nnT

nn

]0[,

)1(

2

1

2

1

2

1

Page 23: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What’s common to SHM schemes?What’s common to SHM schemes?

• Inherently Centralized – Global nature of modes naturally leads to centralized algorithms for detection and localization.

• Can leverage local computation – Almost none of the schemes uses data in its raw form

• ARMA/AR models need coefficients

• Modal frequency based schemes need to use the estimated spectrum

• Compute these quantities locally and transmit instead of raw data.

• 40 ARMA coefficients instead of 5000 samples (over 99% savings!!!)

• Little or no collaboration/aggregation – most algorithms do not require inter-node collaboration (eg SVD is hard to decentralize)

Page 24: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

How many sensors would a typical How many sensors would a typical structure need?structure need?

• Strategies for deploying sensors

• Deploy a tri-axial sensor at the end of every member (damage localization/member)

• Divide the structure into sections and deploy a tri-axial sensor at every corner (damage localization/section)

• Number of sensors determines the granularity of localization (per floor? Per column?)

• A real structure can have several 100s of members/sections

• Local computation is absolutely critical

Page 25: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What are the requirements of What are the requirements of SHM schemes?SHM schemes?

• High data rates – 100 sensor will generate a few Mbps of data

• Reliable Delivery – SHM algorithms do not tolerate sample losses

• Time Synchronization - Required by most schemes

• error in time-synchronization manifests as phase error in modes

• error ~ , the higher the modal frequency the more accuracy one needs

• For 1% error in a 20Hz mode, an accuracy of about 100

• Local computation – data acquisition system based solutions will not scale

tf2

s

Page 26: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for a programmable sensor network based SHM system

• A prototype – implementation and deployment

• What next?

Page 27: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Recall Our Goal…Recall Our Goal…

• We want a system that SHM engineers can program … in Matlab/C

• An SHM engineer should be able to write and test variety of algorithms without having to re-program the motes

• The system should be evolvable – a if better mote platform come, the SHM engineer should not need to rewrite his code

Page 28: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Typical operation of an SHM systemTypical operation of an SHM system

• Sensors collect noise unless the structure is shaking!!!

• Ambient Schemes – rely on significant event (heavy wind, passing truck)

• Forced Schemes – rely on actuators (impact hammers)

• Structural Response lasts a few seconds!!!

• Sensors sleep unless an event occurs or the users requests actuators to test

• Sleep --- test/significant event ---- collect data and locally process --- transmit to central location --- sleep (wake once a day/ once a few hrs)

• SHM systems will be Triggered Systems

Page 29: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Architecture Design DecisionsArchitecture Design Decisions

• Two-level Hierarchy – A higher more endowed layer is required to manage the aggregate data rates generated by the motes.

• Isolate Application code from mote code – Mote class devices provide a generic task interface but no application specific code

• getSamples(startTime, noSamples, sampFreq, axis)

•getFFTSamples(startTime,noSamples,sampFreq,axis,fftSize)

• actuateStructure(startTime,type, parameters)

• conveyed to motes as tasking packets by gateway-class devices

Page 30: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What does code isolation buy us?What does code isolation buy us?

• Reusability – Application programmers can use the generic task interface and write many different SHM applications.

• Basic SHM library functions can me provided on motes fft, auto-correlation, ARMA coefficient estimation, spectral estimation etc.

• Evolvability – If a new mote comes along with greater processing power, just add new functionality, no need to rewrite application.

• Gateway class nodes translate C/Maltab application code into mote tasking commands

Page 31: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for a programmable sensor network based SHM system

• A prototype – implementation and deployment• What next?

Page 32: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

We have a prototypeWe have a prototypefunction shifts = getModalShiftsFromBuilding()

% create a group for sensorsgidSensors = NetSHMCreateGroup([1,2,3,4]);

%create a group for actuatorsgidActuators = NetSHMCreateGroup([5]);

%actuate after 22 secondsNetSHMCmdActuate(gidActuators,22);

%collect structural response starting 20 seconds from now,% 4000 samples at 200Hz,along x-axis only,samples = NetSHMCmdGetSamples(gidSensors,20,200,1,4);

%find modal frequenciesmodes = findModes(samples);%read original modesload OriginalModes;shifts = findModalFreqShifts(modes,OriginalModes);

• A complete SHM test

•Matlab API

• Matlab functions implemented as wrappers over C functions

• Platform MicaZ and starGates

Page 33: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

The StacksThe Stacks

Page 34: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

The API The API

• Groups – Every task is addressed to a group of sensors/actuators

• Create, AddNodes, DeleteNodes, ClearGroup etc

• Create returns a handle to the group

• Tasks – task(groupId, parameters)

• getSamples, getFFTSamples, getXCorrSamples, getModalFreqs, actuate etc.

Page 35: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Mote Tasking LibraryMote Tasking Library

• Translates API commands into command packets to motes

• Uses TimeSynch Module to translate global time to sensor network time

• Dispatches command packets using the Reliability Layer

• Delivers results to applications according to API specifications

• A collection of C and Matlab Mex files

Page 36: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Reliability LayerReliability Layer

• Transactional Delivery – Application expects results asynchronously

• Application issues a task

• Mote Tasking library breaks it up into commands

• Opens a connection to Reliability layer and sends command packet

• Reliability layer keeps connection open and forwards result packets to Mote Tasking Lib

• Mote Tasking Library aggregates results and returns to applications

• Takes care of out of order delivery

• Can handle several applications simultaneously

• OneShot Delivery – Application does not expect any results (e.g. Actuate)

Page 37: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Time SynchronizationTime Synchronization

• Use FTSP

• Small modifications for compatibility with our code

• We use 28.8Khz timer and get accuracy to a few 100micro-sec

• All motes are synchronized to a single mote

Page 38: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

RoutingRouting

• Does not require any-to-any routing

• starGates to motes

• mote to starGates

• starGates to starGates

• both communication end points are never motes

• Routing Modules used

• starGate to starGate - a distance vector routing scheme, also passes on routes to motes

• motes to starGates – CENS Extensible Sensing System,

• starGates to motes – each node periodically transmits list of nodes in its sub-tree to its

parent, the parent keeps a pointer on the reverse path

• We are still investigating better choices for routing

Page 39: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Sensing HardwareSensing Hardware

• MDA400 vibration cards from Crossbow

• high quality low power vibration sensing

• 16-bit samples, on board storage (64k)

• 0-20000Hz sensing

• 4 simultaneous channels

• driven by a micaZ

• Accelerometers

• high sensitivity (1v/g)

• low noise

• Actuators

• off-the shelf door latch devices

• motor control board interfaced to micaZ

Page 40: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

DeploymentDeployment

Seismic Test Structure

Scaled Building Model

Page 41: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Damage Detection and LocalizationDamage Detection and Localizationon scaled modelon scaled model

• Building Details

• 48 inches high, 4 floors, 60 lbs

• Floors –1/2 x 12 x 18 aluminum plates

• steel 1/2 x 1/8 inch steel columns

• 5.5 lb/inch spring braces

• 4 actuators on the top floor

• 8 motes, 2/floor, dual axis, 200Hz, 2 starGates

• 4 Test Cases

• braces from floor 4 removed

• braces from floor 3 removed

• braces from floor 2 removed

• braces from floor 2 and 4 removed

Page 42: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

Performance Analysis on Performance Analysis on Seismic Test StructureSeismic Test Structure

• Structure details

• Full scale imitation of a hospital ceiling (28’ by 48’)

• electric lights, drop ceiling, water pipes, fire sprinklers

• 55,000 lb actuator, 10 inch stroke, manually operated right now

• 15 micaZ motes, 2 starGates, 200Hz

• Latency and robustness to failure

• One starGate carrying most motes killed

• all samples recovered

• 3000 samples in about 5 minutes

Page 43: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

AgendaAgenda

• What’s the talk about ?

• What’s structural health monitoring (SHM)?

• SHM techniques and their impact on sensor network design

• Architecture design for a programmable sensor network based SHM system

• A prototype – implementation and deployment

• What next?

Page 44: UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory A Programmable Sensor Network Based Structural Health Monitoring System Krishna Kant Chintalapudi

UNIVERSITY OFSOUTHERN CALIFORNIA

Embedded Networks Laboratory

What next?What next?

• Develop schemes that allow aggressive local computation

for damage localization.

• Remotely actuate the Seismic Test Structure

• Developing local actuators for the Seismic Test Structure

• Damage Detection and Localization on the Seismic Test Structure

• Experiments on real bridges and structures with large scale deployments


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