Structural Health Monitoring Using Smart Sensors - ideals ...

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Table 8.3. Breakdown of the Time Spent During DCS for SHM Task 1. Parameter injection to cluster heads and the manager node Time (sec) With Debugging Purpose Tasks Time (sec) Without Debugging Purpose Tasks 70 0 2. Time synchronization 6 6 3. Sensing 50 50 4. Query 10 10 5. Resampling 10 10 6. Query 5 5 7. Report to the base station 5 0 8. Communication between the base station and a PC 12 0 9. Repeat 7 and 8 (# of nodes x 3 axis -1) times. ~ 493 (=17x29) 0 10. Multicast the NExT parameters. 5 5 11. Multicast reference signals. 6 6 12. Repeat 10 and 11 (# of cluster head nodes-1) times. ~ 22 (=11x2) 22 13. NExT 42 42 14. Repeat 13 (# of sensor communities a node belongs to) times. ~84 (=42x2) 84 15. Report correlation functions to cluster heads and the base station. 16. Communication between the base station and a PC 17. Repeat 15 and 16 (# of cluster heads x # of leaf nodes in a community x 2 - 1) times. 6 6 6 0 ~420 (= 35x12) 174 18. ERA/DLV 150 150 Total 1,402 570 seconds spent for this reporting are saved. Also, correlation function reports do not need to be sent to the base station and the PC. Six seconds spent for the communication in task 16 147
are saved. The 420 seconds listed at task 17 can be reduced by a factor of two. Furthermore, only the beginning portion of the correlation function is utilized to construct the Hankel matrix in Eq. (6.8), while the current system reports the entire correlation function to the base station and cluster head nodes. Therefore, the report to the cluster heads can be even shorter, though its contribution toward shortening the SHM execution is not counted here. Subtracting the time spent for these debugging tasks from the 1,402 seconds makes the total time for the SHM strategy approximately 676 seconds; the usage of 400 MHz mode of the CPU may further shorten the time necessary to execute the SHM strategy. In summary, about one minute of sensing and 9 minutes of post processing is necessary for the current SHM system without sending debugging information to the base station. Note that the time needed for sensing may increase if natural frequencies of a structure appear in a lower frequency range. As for the time for post processing, the time does not increase as the number of sensor community increases. Though tasks 12, 14 and 17 in Table 8.3 seem to be proportional to the number of sensor communities, they are proportional only when the number of communities is small. When one cluster is out of the communication range of another cluster, these two clusters can perform the tasks in 12, 14, and 17 simultaneously. <strong>Monitoring</strong> damage on a large structure requiring only about nine minutes of data processing in addition to sensing time is an appealing feature of this approach. 8.7 Battery life The battery life of the Imote2 while running the proposed SHM strategy implementation is estimated in this section. When Imote2s performed sensing, the change in the supply voltage to the Imote2s is recorded. In contrast to the previous section, sensing was repeated four times in this experiment. That is, tasks 2 to 14 in Table 8.3 were repeated four times. The voltage values of five Imote2s are read after parameters are initialized, after the first sensing is performed, after reporting to the base station is completed, after the second, third, and fourth measurement finishes, and at the end of ERA and DLV method applications. Figure 8.12 shows the change in the supply voltage to the Imote2s. One round of measurement consumes about 0.03 to 0.08 V. Note that sometimes sensing fails and is repeated until successful measurement is performed. When repetition takes place many times, the decrease in the voltage is considered significant. If sensing becomes more stable with less sensing failures, the number of repetitions will decrease reducing the power consumption per round of sensing. While Imote2s repeat sensing four times for the data shown in Figure 8.12, monitoring of full-scale structures may not need repeated measurements, thus reducing the power consumption per monitoring event. The advantage of repeating sensing is to reduce the effect of observation noise by increasing the number of averages in the spectral densities in Eq (5.1). The SHM implementation explained in sections 8.2 to 8.5, as well as that in section 8.8, measures acceleration responses of the truss only once, yielding 21 averages. From the damage detection results shown in section 8.4, 21 averages seem to be 148
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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
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where R xxref is a vector consistin
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Time (sec) 80 60 40 20 0 -20 -40 -6
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Difficulties encountered in realizi
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1.8715 x10 5 #ofdatablocks 1.871 me
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Before this decimation is applied,
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where xn is the original signal,
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Timer firing every T3 110 data poin
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locks before or after the current b
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Chapter 6 ALGORITHMS In this chapte
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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 and 134: 1 2 Node ID 102 RETAKE 1 3 4 INCONS
Page 135 and 136: Initialization: within local sensor
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: for a SHM strategy does not necessa
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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