1. 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

2. 1 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, NYC African Elephant

3. How long can I keep it like this? I suggest an immediate surgery to repair it It’s a torn ligament You are sick! It’s in the kneeWhere? How bad is it? A SHM system should be able to successfully carry out 4 tasks 2Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 Damage detection Damage localization Damage quantification Assessment of the remaining lifetime of the structure

4. Outline of the Talk 3 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, f i )  works iteratively (no need to store the acceleration signals)  the number of samples (N) does not need to be a number power of 2 4 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 GA computations sample acquisition sampling START t sampling END

6. Goertzel Algorithm Parameters 3 key parameters (set by the end-user):  Sampling frequency (f s )  Distance (d b ) between two consecutive bins on the frequency axis (resolution r = 1/ d b )  Vector of frequencies of interest (f i ) GA can be thought of as a 2 nd -order IIR filter for each frequency of interest: 5 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

7. 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 ( f i ): Coefficients (c) used in the iterations: Equations iteratively executed by the nodes during the sampling: Squared magnitude of the spectrum: 6 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 s i : last collected sample q 1 and q 2 store the results of the two previous iterations

8. ...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: 7 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 s i and s j : sensor nodes (f i,f 2 ): range of frequencies of interest REF: reference (undamaged) TEST: current condition (damaged?)

9. Flow of the Application 8 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

10. 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/s 2 Sensor Nodes Hardware 9 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

11. Testbed Setup 10 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 5 6 8 1 2 4 7 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 D 1, D 2, D 3, D 4 :500 g weight D 5 : 27.6% stiffness reduction D 6 : 55.2% stiffness reduction sensor node Random noise excitation

12. Experimental Validation 11 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

13. Experimental Validation 12 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

14. Experimental Validation 13 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

15. Experimental Validation 14 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

16. Experimental Validation 15 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 D 5 : 27.6% stiffness reduction D 6 : 55.2% stiffness reduction

17. Centralized VS Distributed Life time increase: 52% 16 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

18. Centralized VS Distributed Latency reduction: 80% 17 Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

19. Thank You! 18 maurizio.bocca@tkk.fi http://autsys.tkk.fi/MaurizioBocca Questions?