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Novel Sensing Networks for Intelligent Monitoring ( Newton)

Novel Sensing Networks for Intelligent Monitoring ( Newton). Z Q Lang, H Chen, T Dodd Department of Automatic Control & Systems Engineering University of Sheffield. 9 July 2013. Outline. Time domain modelling and frequency domain analysis – Core signal processing technique of

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Novel Sensing Networks for Intelligent Monitoring ( Newton)

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  1. Novel Sensing Networks for Intelligent Monitoring (Newton) Z Q Lang, H Chen, T Dodd Department of Automatic Control & Systems Engineering University of Sheffield 9 July 2013

  2. Outline • Time domain modelling and frequency domain • analysis – Core signal processing technique of • the autonomous monitoring system to be developed • by Newton Project • Application to processing data from the • new PEC sensing module developed at Newcastle • An idea to apply the approach to the signal • analysis in the novel RFID based PEC sensing • technology being developed at Newcastle • Conclusions

  3. Autonomous monitoring system to be developed • by the Newton Project Time Domain Modelling and Frequency Domain Analysis

  4. Why modelling systems, and why analysing system models in the frequency domain? Excitations Response Signals Infrastructural Systems Signal feature based monitoring Modelling Process Model frequency domain feature based monitoring Result A Result B • Result A represents system behaviours while Result B represents the • system properties. • The frequency domain analysis of system properties can reveal unique • features of monitored systems.

  5. Experimental tests using the new PEC sensing module developed at Newcastle Excitation New PEC sensing module Response sample Defects

  6. Illustration of the time domain modelling and frequency domain analysis process Excitation Structural Models Modelling Responses Models’ Frequency Domain Feature Index Extraction of models’ frequency domain features

  7. Data Analysis Results Case 1: 0mm defect Case 2: 2mm defect Case 3: 4mm defect Case 4: 6mm defect Case 5: 8mm defect Case 6: 10mm defect Case 7: 12mm defect Case 8: 14mm defect Case 9: 16mm defect

  8. An illustration of RFID Sensing and the idea of application of time domain modelling and frequency domain analysis approach Output Input 2 (1.95kHz) Input 1 A system (composed of RFID reader, tag and associated sample area) (125kHz Pulse) Input 1 Input 2 Output

  9. Evidence of possible system nonlinearities FFT of Output 125KHz 2*125KHz 3*125KHz Output of RFID system 1.95KHz 3*1.95KHz 2*1.95KHz 4*1.95KHz 125KHz 125KHz-1.95KHz 125KHz-3*1.95KHz

  10. Conclusions • The time domain modelling and frequency domain analysis approach has been • successfully applied to analyse data from the new PEC sensing module developed • at Newcastle. • The RFID sensing system may need to be considered as a two inputs and one output • nonlinear system so nonlinear system time domain modelling and frequency • domain analysis should be used to resolve the associated autonomous monitoring • problems. • Plan for next step: • - Investigating accuracy issues with defect detection using PEC sensing and • time domain modelling and frequency domain analysis. • - Studying the application of time domain modelling and frequency domain analysis • to RFID sensing based autonomous monitoring. • - Studying mobile robot based implementation technology.

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