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Structural Health Monitoring in WSNs by the Embedded Goertzel Algorithm. Maurizio Bocca, M.Sc. Department of Automation and Systems Technology. Aalto University School of Electrical Engineering. www.wsn.tkk.fi. What is Structural Health Monitoring?. African Elephant. Brooklyn Bridge, NYC. - PowerPoint PPT Presentation
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Structural Health Monitoringin WSNs by the
Embedded Goertzel Algorithm
Maurizio Bocca, M.Sc.Department of Automation and Systems
TechnologyAalto University School of Electrical Engineering
www.wsn.tkk.fi
2
What is Structural Health Monitoring?
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
Accurate diagnosis of the health of civil infrastructures from data collected by sensors
Brooklyn Bridge, NYCAfricanElephant
3
How long can I keep it like this?
I suggest an immediate surgery to repair itIt’s a torn ligamentYou are sick!It’s in the kneeWhere?How bad is it?
A SHM system should be able to successfully carry out 4 tasks
Detect Localize Quantify Assess
COMPLEXITYMaurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
Damage detectionDamage localizationDamage quantificationAssessment of the remaining lifetime of the structure
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Outline of the Talk
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
Goertzel algorithm (GA)
WSN architecture
Experimental evaluation
5
Why the Goertzel Algorithm for SHM?
• Classic application: DTMF• Compared to the FFT, the GA:
allows to efficiently calculate the amplitude of the frequency spectrum at specific bins (frequencies of interests, fi)
works iteratively (no need to store the acceleration signals)
the number of samples (N) does not need to be a number power of 2
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
GA computationssample acquisition
samplingSTART
t
samplingEND
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Goertzel Algorithm Parameters
• 3 key parameters (set by the end-user): Sampling frequency (fs) Distance (db) between two consecutive bins on the
frequency axis (resolution r = 1/ db) Vector of frequencies of interest (fi)
• GA can be thought of as a 2nd-order IIR filter for each frequency of interest:
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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1 2
1
1 2cos 2
i
s
i
fZ
f
fi
s
eH Zf Z Zf
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From the Goertzel Algorithm...
• Number of samples (N) to be collected to obtain the fixed resolution (r):
• Bins (k) corresponding to the selected frequencies of interest (fi):
• Coefficients (c) used in the iterations:
• Equations iteratively executed by the nodes during the sampling:
• Squared magnitude of the spectrum:
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
s
b
fNd
0.5 i
s
N fk
f
2cos 2 kcN
0 1 2
2 1
1 0
iq c q q sq qq q
si: last collected sample
q1 and q2 store the results of thetwo previous iterations
2 2 21 2 1 2i i i ii iX N q q q q c
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...to Transmissibility Functions
• Transmissibility is the result of the interference of vibrations propating and reflecting along the structure
• TFs achieve environmental invariability
• Structural damages modify the spectrums of the acceleration signals collected by the nodes
• Damage indicator:
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
2
1
2
1
2
1 22
,j
j
i
i
f
ss f fs f
sf f
X fT f f
X f
si and sj: sensor nodes(fi ,f2): range of frequencies of interest
1 2 1 2
1 21 2
, ,,
,
j j
i ij
i j
i
TEST REFs ss ss
r s REFss
T f f T f fD f f
T f f
REF: reference
(undamaged)TEST: current condition (damaged?)
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Flow of the Application
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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• Sensinode U100 Micro.2420:• MSP430 MCU (10 kB RAM, 48 kB
Flash)• 500 kB external serial data Flash• CC2420 transceiver (ZigBee,
802.15.4 compatible, 2.4 GHz band, 250 kbps theoretical bandwidth)
• 3 axis digital accelerometer:• ±2g/±6g selectable full scale• 12/16 bit representation• Sensitivity: 76.4 mV/m/s2
Sensor Nodes Hardware
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Testbed Setup
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
5
6
8
1
2
47
3
D3
D4
D1
D2
Electro-Dynamic Shaker
Damaged Cross Bar
WOODEN TRUSS STRUCTURE: 420 cm long, 65 cm wide, 34 cm high, 44 kg
D1, D2, D3, D4:500 g weight
D5: 27.6% stiffness reductionD6: 55.2% stiffness reduction
sensor node
Random noise excitation
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Experimental Validation
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Experimental Validation
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Experimental Validation
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Experimental Validation
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Experimental Validation
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
D5: 27.6% stiffness reduction
D6: 55.2% stiffness reduction
17
Centralized VS Distributed
• Life time increase: 52%
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011
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Centralized VS Distributed
• Latency reduction: 80%
Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011