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Bridge & Structure LaboratoryUniversity of Tokyo
1
Structural health monitoring using Imote2 Tomonori NagayamaAssistant Professor University of Tokyo
07/10/2009
Bridge & Structure LaboratoryUniversity of Tokyo
2
Wireless sensor components -functionality- The Imote2 has promising features. But not all the
functionalities needed in SHM are provided in OS/HW.
Hardware
OS
Middleware
SHM applications
RF
CP
UM
emory
Pow
er
Sensor/
actuator
sensing
Following functionalities are provided as middleware services. Users can utilize them to assemble their own SHM applications Time Synchronization Synchronizaed sensing Reliable communication Efficient data aggregation Others
timesync data aggregation
comm/networking
Bridge & Structure LaboratoryUniversity of Tokyo
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Synchronization basics
Node synchronization Nodes exchange packet and estimate local clock offsets
Time synchronization protocols Reference Broadcast Synchronization (RBS), Timing-sync
Protocol for Sensor Network (TPSN), Flooding Time Synchronization Protocol (FTSP)
t1
t2
t3
Node1 clock
Node2 clock
Node3 clock
T2
T3 +T3
+T2
Global time
Bridge & Structure LaboratoryUniversity of Tokyo
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Time synchronization middleware Based on Flooding Time Synchronization Protocol
(FTSP)
By cascading, this synchronization works on a multihop network
Send packet
Append time stampt1
Obtain reception timet2
Global time = local time + t1-t2+t3
t3
Concept
Bridge & Structure LaboratoryUniversity of Tokyo
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Time synchronization accuracy check Timestamps of receivers are examined
Send packet
Concept Time synchronization
t3Get
globaltime
Getglobaltime
Getglobaltime
Getglobaltime
Getglobaltime
Getglobaltime
Repeat n times
Time synchronization
t3
Bridge & Structure LaboratoryUniversity of Tokyo
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Synchronized Sensing accuracy check
Synchronization accuracy
…
BeaconReply global time
Time synchronization error < 150 Time synchronization error < 150 s. Mostly < 20s. Mostly < 20ss
Difference in returned global time stamps
50 100 150 200 250 300
-80
-40
0
40
80
Repetition
Tim
e (
s)
Bridge & Structure LaboratoryUniversity of Tokyo
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t2+T
t3+T
Synchronization basics -drift-
Drift Due to difference in clock speed of each node, difference among
local times changes (almost linearly) Synchronization error accumulates as time passes after the last
synchronization unless appropriate compensation is performed.
t1
t2
t3
Node1 clock
Node2 clock
Node3 clock
T2
T3 +T3
+T2
Global time
+T3+T
+T2+T
t1+T
Bridge & Structure LaboratoryUniversity of Tokyo
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Time synchronization drift check Difference among local clocks (T)are
examined
Send packet
Time synchronization
t3
Concept
GetT2
GetT3
GetT4
Repeat n times
Get T2+T1
GetT3+T1
GetT4+T1
Get T2+T2
GetT3+T2
GetT4+T2
Bridge & Structure LaboratoryUniversity of Tokyo
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Drift estimation
…
BeaconReply offset
• is almost constant over timeis almost constant over time• Difference in clock rates can be as large as 50 Difference in clock rates can be as large as 50 s/ss/s
0 40 80 120 160-4000
0
4000
8000
Time (s ec)
Drif
t(
s)
Clock drift
T
(s
)
However, time synchronization of However, time synchronization of the nodes does not provide the nodes does not provide
synchronized sensing.synchronized sensing.
However, time synchronization of However, time synchronization of the nodes does not provide the nodes does not provide
synchronized sensing.synchronized sensing.
Bridge & Structure LaboratoryUniversity of Tokyo
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Toward synchronized sensing EVEN If a command to start sensing is
issued at the same time, the execution timing is different
Sampling timing has individual difference
node1
“Start sensing”
node2
node3
Sampling timing time
Actual start t1
t2
t3
t1 != t2 != t3
Bridge & Structure LaboratoryUniversity of Tokyo
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Two approaches for synchronized sensing
Strict HW control of sampling timing Sampling has high priority than
other tasks. No need for post processing Other tasks are delayed.
Resampling based approach Sensing starts at the approximately
same time . Resampling based on accurate
timestamping Less requirement on HW Timestamp + Resampling + linear
interpolation -> VERY accurate synchronized sensing is realized
Strict HW control
HW control
Resample
Bridge & Structure LaboratoryUniversity of Tokyo
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Resampling basics
fsfs11
L MLL MML MLL MML MLL MML MLL MM
L MLL MML MLL MML MLL MM
fsfstargettarget
upsample filter downsample
To eliminate aliasing components
Resampling without distortion in signal
Bridge & Structure LaboratoryUniversity of Tokyo
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Combination of resampling and linear interpolation
L MLL MML MLL MML MLL MML MLL MM
L MLL MML MLL MML MLL MM
upsample filter downsample
What if we need data at these timing ? Linear interpolation
Bridge & Structure LaboratoryUniversity of Tokyo
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Cross spectrum
Synchronized sensing accuracy checkAccuracy of synchronization among signals
Cross spectral densities among sensors have almost flat phase meaning accurately synchronized signals
1 degree at 100Hz 1/360/100 = 28s
synchronization error
1x t
2 1x t x t t
Fourier transform 1X 2 1 expX X i t *
1 2X X t
Bridge & Structure LaboratoryUniversity of Tokyo
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packet
Reliable communication Redundant packet
transmission Packet retransmission:
Same packets are transmitted more than once
Erasure codelost packets can be reconstructed
To transfer Send
13 15 21 13
To transfer Send
13 15 21
Received
13 15 21
Reconstruct13 15 21
13 15 21 13 15 21x xPacket loss
13 15 21 13+15+21xPacket loss
Received Reconstruct
However burst loss may happen, then ?
x x
x x
15 21 13 15 21
13 15 21 13+15+21
13
Bridge & Structure LaboratoryUniversity of Tokyo
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Reliable communication Acknowledgement based approach
To transfer
13 15 21
Send
13
ACK
15 21
13 15 21
Reconstruct Received
13 15 21
15
ACK ACK
Reliable but slow to transfer a large amount of data
Bridge & Structure LaboratoryUniversity of Tokyo
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Reliable communication
Acknowledgement based approach: fewer ACK packets
To transfer
13 15 21 …16
Send
13 21
13 15 21 …16
Reconstruct
13 15 21
15
ACK15 is
missing
Reliable and fast to transfer a large amount of data
16
16
…
15
15
All received
Bridge & Structure LaboratoryUniversity of Tokyo
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Efficient data aggregation
Application specific knowledge is utilized to efficiently perform data aggregation
Data
Efficient data aggregation
Information
Application specificknowledge
Bridge & Structure LaboratoryUniversity of Tokyo
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Application specific knowledge -Natural Excitation Technique-
Definition:Estimate:
iR Correlation function
ref ref refx x x x x xMR CR KR 0
0 i i refR E x t x t
*
1
1ˆ dn
xy i iid
G X Yn T
i i refR E x t x t
1 ˆˆxy xyR G F
( Cross Spectrum Density estimation )
Natural Excitation Technique (NExT)“Correlation functions satisfies EOM for free vibration”
Data compression through averaging
1/20 - 1/10(nd = 10-20)
ix
t t t t Mx Cx Kx f
Measurement
Subsequently decomposed into modal vibrations
Bridge & Structure LaboratoryUniversity of Tokyo
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Node 1x1
Node 2x2
Node 3x3
Node 4x4
Node nsxns
. . .Node 5x5
1 1ˆ
i iE x t x t R i =1,2,…,ns
Centralized data aggregationCorrelation function estimation
Requires signals from 2 nodes 2 approaches
Centralized implementation O(N·nd·ns)
Distributed implementation
*
1
1ˆ dn
xy i iid
G X Yn T
1 ˆˆxy xyR G F
1d sN n n Transmission
Bridge & Structure LaboratoryUniversity of Tokyo
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Packet transfer Broadcast and unicast
Broadcast: 1-to-”others in the range”
Unicast: 1-to-1 Specify the destination by
node ID Basically broadcast, but
others ignore.
Unicast
Broadcast
Bridge & Structure LaboratoryUniversity of Tokyo
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Correlation function estimation Requires signals from 2 nodes 2 approaches
Centralized implementation O(N·nd·ns)
Distributed implementation O( N(nd+ns))
Node 1x1
Node 2x2
Node 3x3
Node 4x4
Node nsxns
. . .Node 5x5
1 1 11ˆE x t x t R
12R̂ 13R̂ 14R̂ 15R̂ 1ˆ
snR
Distributed Data Aggregation
*
1
1ˆ dn
xy i iid
G X Yn T
/ 2 1d sN n N n Transmission
1 ˆˆxy xyR G F
Data transfer requirement is a primary Data transfer requirement is a primary factor for power consumption. factor for power consumption.
Distributed implementation has an Distributed implementation has an advantage advantage
Ex)Ex)
NN = 1024, = 1024, nndd=20, =20, nnss = 15= 15
Centralized implementation Centralized implementation 286,720 286,720Distributed implementation Distributed implementation 27,648 27,648A reduction factor of 10.4A reduction factor of 10.4
Data transfer requirement is a primary Data transfer requirement is a primary factor for power consumption. factor for power consumption.
Distributed implementation has an Distributed implementation has an advantage advantage
Ex)Ex)
NN = 1024, = 1024, nndd=20, =20, nnss = 15= 15
Centralized implementation Centralized implementation 286,720 286,720Distributed implementation Distributed implementation 27,648 27,648A reduction factor of 10.4A reduction factor of 10.4
Bridge & Structure LaboratoryUniversity of Tokyo
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Integration of middleware services into applications Example1 Distributed Computing Strategy
for SHM Example2 Railroad bridge vibration
monitoring
Bridge & Structure LaboratoryUniversity of Tokyo
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Distributed Computing Strategy for SHM
Sensing
NExT
SDLV
DCS logic
Cluster formation
ERA
(常時微動計測を仮定)1. Vibration measurement ambient vibration measurement2. Modal analysis in each cluster
OutPut: Natural frequency, Mode shape, A,C matrices
Method: NExT, ERA
3. Damage assessment in each communityOutput: Damage locationMethod: Stochastic damage locating vector
4. Synthetic judgment among cluster headsOutput: Damage locationMethod: DCS logic
DCS flow chart
Bridge & Structure LaboratoryUniversity of Tokyo
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DCS implementation middleware
Reliable communication Synchronized sensing Efficient data
aggregation
Numerical library FFT SVD Eigensolver sort
Static stress analysis (a part of damage detection)
All the tasks are predefined. Once parameters are injected to the network, the Imote2s autonomously perform damage identification.
Bridge & Structure LaboratoryUniversity of Tokyo
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Experimental Verification Ten Imote2s, 3 clusters
autonomously monitor the 3D truss scale model
Longitudinal & vertical measurements
Damage simulated by an element with a small cross-section is localized by Imote2s
53% cross section reduction
Bridge & Structure LaboratoryUniversity of Tokyo
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The SDLV method
The damaged element 8 has small stress indicating damage.
0
0.2
0.4
0.6
0.8
1
3 4 5 6 7 8 9 1011 1112 1314 151617 18197 8 9 10 111213 1415Element ID
No
rma
lize
d a
ccu
mu
late
d s
tre
ss
Threshold0.3
Damaged element
Bridge & Structure LaboratoryUniversity of Tokyo
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Railroad bridge monitoring application
Node wakeup based on train schedule Extract time traffic vibration Data processing
Cluster head
Leaf node
Leaf node
Leaf node
Detailed modal analysis of viaducts from ambient vibration is Detailed modal analysis of viaducts from ambient vibration is non-trivial exploit traffic vibration ⇒ non-trivial exploit traffic vibration ⇒
After train passageAfter train passage natural frequenciesnatural frequencies linear damagelinear damage
During train passagDuring train passagee ::Vibration amplitude Vibration amplitude abnormal abnormal vibrationvibrationCoherence functionCoherence function non-linearitynon-linearity
Report to BS
Amplitude levelcoherence function
Modal identification
Signal extraction
Sensing
Wakeup
Bridge & Structure LaboratoryUniversity of Tokyo
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Service-Oriented Architecture Service-Oriented ArchitectureService-Oriented Architecture (SOA) in the SHM
Toolkit simplifies SHM software development Applications are comprised of manageable, modular
servicesservices that exchange data in a common format The middleware frameworkmiddleware framework connects the services by
providing communication and coordination
SDLV
Numerical ServicesApplication Services Foundation Services SHM Application
Bridge & Structure LaboratoryUniversity of Tokyo
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SHM Toolkit Contents Foundation services
Universal sensing Time synchronization Reliable communication Numerical library
Application services Correlation function estimation (CFE) Eigensystem Realization Algorithm (ERA) Stochastic Damage Load Vector (SDLV) Stochastic Subspace Identification (SSI) Synchronized sensing
Test applications, tools and utilities Radio & antenna testing Data acquisition (local and remote) Test applications for each component of the toolkit
Bridge & Structure LaboratoryUniversity of Tokyo
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http://shm.cs.uiuc.edu