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Structural Health Monitoring in WSNs by the Embedded Goertzel Algorithm

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.

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Structural Health Monitoring in WSNs by the Embedded Goertzel Algorithm

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  1. Structural Health Monitoringin WSNs by theEmbedded Goertzel Algorithm Maurizio Bocca, M.Sc. Department of Automation and Systems Technology Aalto University School of Electrical Engineering www.wsn.tkk.fi

  2. What is Structural Health Monitoring? African Elephant Brooklyn Bridge, NYC Accurate diagnosis of the health of civil infrastructures from data collected by sensors Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

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

  4. 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 atspecific 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 t sampling START sampling END GA computations sample acquisition

  6. 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

  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 (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 si: last collected sample q1 and q2 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: • Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 si and sj: sensor nodes (fi ,f2): range of frequencies of interest REF: reference (undamaged) TEST: current condition (damaged?)

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

  10. Sensor Nodes Hardware • 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 • Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011

  11. Testbed Setup WOODEN TRUSS STRUCTURE: 420 cm long, 65 cm wide, 34 cm high, 44 kg • Maurizio Bocca – ICCPS 2011, Chicago, IL, USA, 14.4.2011 sensor node D1, D2, D3, D4:500 g weight 8 D3 4 7 D1 6 3 D4 2 5 D2 1 Damaged Cross Bar D5: 27.6% stiffness reduction D6: 55.2% stiffness reduction Electro-Dynamic Shaker Random noise excitation

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

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

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

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

  16. 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

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

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

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