Structural Health Monitoring Using Smart Sensors - ideals ...

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Initialization: without mass perturbation Inject parameters • NodeID • Measurement direction • # of samples • NExT/ERA parameters Time synchronization • Send beacon signal • Estimate local clock offset • Estimate clock drift Sensing • Update local clock offset • Start sensing • Copy acquired data to global variables • Acquire timestamp of each block of data • resample and synchronize the measured data Report to the base station Correlation function estimation • broadcast data • cross-spectral density estimation • ifft •averaging yes Need more averaging ? Report to the cluster heads and base station ERA no Report to the base station Initialization: with mass perturbation Inject parameters • NodeID • Measurement direction • # of samples • NExT/ERA parameters • Modal parameters from the previous step Time synchronization • Send beacon signal • Estimate local clock offset • Estimate clock drift Sensing • Update local clock offset • Start sensing • Copy acquired data to global variables • Acquire timestamp of each block of data • resample and synchronize the measured data Report to the base station Correlation function estimation • broadcast data • cross-spectral density estimation • ifft • averaging yes Need more averaging ? no Report to the cluster heads and base station ERA/Mass normalization constant estimate Report to the base station Figure 7.24. The block diagram of mass normalization constant estimation. At the beginning, parameters such as node IDs of the leaf nodes and measurement directions are injected to the manager sensor and cluster head nodes. Also, these nodes are notified which of the four sets of measurements shown in Figures 7.24 and 7.25 needs to be performed. Information from the base station or users is transferred only at this stage. It is considered possible to inject these parameters only once and store them on nonvolatile memory. In this way, networks of smart sensors can work as autonomous stand-alone systems. At the end of parameter injection, time synchronization begins. To increase the possibility of successful time synchronization, and to estimate clock drift, time synchronization is repeated multiple times. Time synchronization is also performed later between measurements. 127
Initialization: within local sensor communities Inject parameters • NodeID • Measurement direction •# of samples • NExT/ERA parameters • Modal parameters from the previous step Time synchronization • Send beacon signal • Estimate local clock offset • Estimate clock drift Sensing • Update local clock offset • Start sensing • Copy acquired data to global variables • Acquire timestamp of each block of data • resample and synchronize the measured data Report to the base station Correlation function estimation • broadcast data • cross-spectral density estimation •ifft •averaging yes Need more averaging ? no Report to the cluster heads and base station ERA Report to the base station Inject parameters •NodeID • Measurement direction •# of samples • NExT/ERA parameters • Modal parameters from the previous step • mass normalization constants Time synchronization • Send beacon signal • Estimate local clock offset • Estimate clock drift Sensing • Update local clock offset • Start sensing • Copy acquired data to global variables • Acquire timestamp of each block of data • resample and synchronize the measured data Report to the base station Correlation function estimation • broadcast data • cross-spectral density estimation • ifft •averaging yes Need more averaging ? Report to the cluster heads and base station ERA/DLV <strong>Monitoring</strong> no Report to the base station Figure 7.25. The block diagram of monitoring in a sensor community. Synchronized sensing is performed in the manner described in section 5.3. The number of sensing samples is set to be 11,264. The raw data is sent back to the base station for debugging purposes. This reporting allows numerical operations on Imote2s to be reproduced on the PC; the validity of data processing on the Imote2 can also be examined. Centrally collecting all of the data takes a substantial amount of time and can be eliminated once this DCS system is fully developed. Correlation functions are then estimated in a distributed manner. The model-based data aggregation approach explained in section 5.1 is employed. If the number of averages in correlation function estimation, n d in Eq. (5.1), is smaller than the predetermined value, sensing, reporting to the base station, and correlation function estimation are repeated (see Figures 7.24 and 7.25). 128
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NSEL Report Series Report No. NSEL-
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The Newmark Structural Engineering
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Contents Page CHAPTER 1 INTRODUCTIO
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9.2.3 Power harvesting . . . . . .
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damage/deterioration is intrinsical
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scalability to a large number of sm
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logic circuit. However, FPGAs are m
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easons, smart sensors spatially dis
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Figure 2.1. EYES project sensor pro
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Table 2.1. Smart Sensor Specificati
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While a number of sensor boards for
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need to be within the coordinator's
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eing examined (Moore et al., 2001).
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structural damage, the analysis to
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that sensor installation cost inclu
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(2004) indicated that this accelero
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different type of sensors connected
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Chapter 3 SHM ARCHITECTURE This cha
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Modal operation Several modal opera
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its spatial gradient, which is clai
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Manager node Cluster head node Leaf
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commands; execution timing cannot b
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Table 3.2. Desirable Characteristic
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Table 3.2. Desirable Characteristic
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Figure 4.1. Foil strain gage. strai
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dB A s Figure 4.4. AA filter design
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8.32F 3V 3.45F 3V Input 1K 1K 1K 1K
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Strain ( ) 80 w/o Digital Filter 60
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performance was experimentally vali
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anges from 10 to 20. If 50 percent
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Node 1 x1 i =1,2,…,ns E x 1
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Correlation function estimate (m 2
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Figure 5.5. Effect of data loss and
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Frobenius norm. Because only a limi
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90 80 70 60 50 Node1 Node2 Node3 No
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On reception of a NACK, the sender
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Sender SendLData.send() Pack parame
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If there are missing packets, the r
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Sender SendLData.bcast() Pack param
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Sender Send a packet • Sender sen
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utilized in SHM applications follow
Page 83 and 84: where R xxref is a vector consistin
Page 85 and 86: Time (sec) 80 60 40 20 0 -20 -40 -6
Page 87 and 88: Difficulties encountered in realizi
Page 89 and 90: 1.8715 x10 5 #ofdatablocks 1.871 me
Page 91 and 92: Before this decimation is applied,
Page 93 and 94: where xn is the original signal,
Page 95 and 96: Timer firing every T3 110 data poin
Page 97 and 98: locks before or after the current b
Page 99 and 100: Chapter 6 ALGORITHMS In this chapte
Page 101 and 102: The eigenproblem of the system matr
Page 103 and 104: where M 0 is the mass matrix of the
Page 105 and 106: same community, eliminating the nee
Page 107 and 108: measurement or mass perturbation ca
Page 109 and 110: these algorithms are implemented on
Page 111 and 112: 10 0 10 -2 10 -4 0 500 1000 1500 20
Page 113 and 114: Table 7.1. SVD Accuracy Check on Ma
Page 115 and 116: 7.1.4 Complex matrix inverse A comp
Page 117 and 118: Figure 7.9. Details of the joint (G
Page 119 and 120: Figure 7.13. Amplifier (Gao, 2005).
Page 121 and 122: 0.08 0.2 Acceleration (g) 0.04 0 -0
Page 123 and 124: 3 Imote2_2/Imote2_1 Imote2_1/Siglab
Page 125 and 126: 200 3 Phase of spectral densities (
Page 127 and 128: 1.5 x10-17 Time (sec) Difference in
Page 129 and 130: input forces applied at nodes in th
Page 131 and 132: CH1 CH2 CH3 …. CHn-1 CHn Initiali
Page 133: 1 2 Node ID 102 RETAKE 1 3 4 INCONS
Page 137 and 138: PC Base station Manager sensor Clus
Page 143 and 144: Table 7.7. Instructions in DCS Code
Page 145 and 146: EXPERIMENTAL VERIFICATION Chapter 8
Page 147 and 148: 8 x10-3 Time (sec) Correlation func
Page 149 and 150: Table 8.2. Mass Normalization Const
Page 151 and 152: 1 2 Node ID 67 RETAKE 0 3 4 ID 67 #
Page 153 and 154: for a SHM strategy does not necessa
Page 155 and 156: are saved. The 420 seconds listed a
Page 157 and 158: 1 1 Normalized accumulated stress m
Page 163 and 164: Table 8.5. Summary of False Damage
Page 165 and 166: Table 8.7. Summary of False Damage
Page 167 and 168: e scalability, autonomous distribut
Page 169 and 170: The lowest frequencies of interest
Page 171 and 172: structural vibration is considered
Page 173 and 174: REFERENCES Actis, R. L. & Dimarogon
Page 175 and 176: Casciati, F., Faravelli, L, Borghet
Page 177 and 178: Feldman, M. & Braun, S. (1995). “
Page 179 and 180: Kling, R. (2003). “Intel Mote: an
Page 181 and 182: Mufti, A. A. (2003). “Integration
Page 183 and 184: Sazonov, E., Janoyan, K., & Jha, R.
Page 185 and 186:
Yi, J.-H. & Yun, C.-B. (2004). “C
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