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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 - PowerPoint PPT Presentation
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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 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
Autonomous monitoring system to be developed by the Newton Project
Time Domain Modelling and Frequency Domain Analysis
Why modelling systems, and why analysing system models in the frequency domain?
• Result A represents system behaviours while Result B represents the system properties.
Infrastructural Systems
Excitations Response Signals
Modelling Process
Model frequency domain feature based
monitoring
Signal feature based monitoring
Result B
Result A
• The frequency domain analysis of system properties can reveal unique features of monitored systems.
-10 -5 0 5 100
0.5
1
1.5
2
2.5
Volta
ge (V
)
Time (s)
-10 -5 0 5 100
0.5
1
1.5
2
2.5
Volta
ge (V
)
Time (s)
Experimental tests using the new PEC sensing module developed at Newcastle
Excitation
Response
sample
New PEC sensing module
Defects
Illustration of the time domain modelling and frequency domain analysis process
Modelling
Excitation
Responses
Extraction of models’
frequency domain features
Structural Models
Models’
Frequency DomainFeature Index
Data Analysis Results
Case 1: 0mm defectCase 2: 2mm defectCase 3: 4mm defect Case 4: 6mm defectCase 5: 8mm defectCase 6: 10mm defectCase 7: 12mm defectCase 8: 14mm defectCase 9: 16mm defect
An illustration of RFID Sensing and the idea of application of time domain modelling and
frequency domain analysis approach
Input 1
Output
(125kHzPulse)
Input 2(1.95kHz)
A system (composed of
RFID reader, tag and associated sample area)
Input 1
Input 2 Output
0 0.01 0.02 0.03 0.04 0.05-0.4
-0.2
0
0.2
0.4
0.6
time(s)
Am
pltid
ue
0 1 2 3 4 5
x 105
-50
0
50
100
Frequency(Hz)
Am
pltid
ue
(a)
0 2000 4000 6000 8000 10000-40
-20
0
20
40
60
80
Frequency(Hz)
Am
pltid
ue
(b)
1.18 1.19 1.2 1.21 1.22 1.23 1.24 1.25 1.26 1.27
x 105
-20
0
20
40
60
80
Frequency(Hz)
Am
pltid
ue
125k-3*1.95k
125k-1.95k
(a)
Output of RFID system
FFT of Output125KHz 2*125KHz 3*125KHz
1.95KHz
2*1.95KHz3*1.95KHz
4*1.95KHz
125KHz
125KHz-3*1.95KHz125KHz-1.95KHz
Evidence of possible system nonlinearities
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.
