Structural Health Monitoring Using Smart Sensors - ideals ...

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Accelerometer Strain gage Strain sensor board Base station Figure 4.11. Experimental setup (a three-story building model and smart sensors). 600 400 Strain sensor board Reference model showed that the wireless strain sensor has a good resolution and that the output is comparable to conventional wired strain sensors (see Figure 4.12). 4.4 Summary Strain ( ) 200 0 -200 -400 Figure 4.12. Strain sensor readings. 10 11 12 13 14 15 16 Time (sec) The customizability of sensor boards was demonstrated using the Berkeley Mote platform. While strain is one of important physical quantities for SHM applications, strain measurement using smart sensors has been rare. No strain sensor board was available for the Berkeley Mote. Strain sensor and AA filter boards were developed and their 49
performance was experimentally validated. Sensor board customization procedures for other smart sensor platforms are considered similar to those demonstrated in this chapter. As demonstrated, physical quantities needed in SHM applications can be measured by customized sensor boards. These physical quantities are processed and transferred by middleware services and algorithms. In the next chapter, middlewere services will be developed. 50
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NSEL Report Series Report No. NSEL-
Page 3 and 4:
The Newmark Structural Engineering
Page 5 and 6: Contents Page CHAPTER 1 INTRODUCTIO
Page 7 and 8: 9.2.3 Power harvesting . . . . . .
Page 9 and 10: damage/deterioration is intrinsical
Page 11 and 12: scalability to a large number of sm
Page 13 and 14: logic circuit. However, FPGAs are m
Page 15 and 16: easons, smart sensors spatially dis
Page 17 and 18: Figure 2.1. EYES project sensor pro
Page 19 and 20: Table 2.1. Smart Sensor Specificati
Page 21 and 22: While a number of sensor boards for
Page 23 and 24: need to be within the coordinator's
Page 25 and 26: eing examined (Moore et al., 2001).
Page 27 and 28: structural damage, the analysis to
Page 29 and 30: that sensor installation cost inclu
Page 31 and 32: (2004) indicated that this accelero
Page 33 and 34: different type of sensors connected
Page 35 and 36: Chapter 3 SHM ARCHITECTURE This cha
Page 37 and 38: Modal operation Several modal opera
Page 39 and 40: its spatial gradient, which is clai
Page 41 and 42: Manager node Cluster head node Leaf
Page 43 and 44: commands; execution timing cannot b
Page 45 and 46: Table 3.2. Desirable Characteristic
Page 47 and 48: Table 3.2. Desirable Characteristic
Page 49 and 50: Figure 4.1. Foil strain gage. strai
Page 51 and 52: dB A s Figure 4.4. AA filter design
Page 53 and 54: 8.32F 3V 3.45F 3V Input 1K 1K 1K 1K
Page 55: Strain ( ) 80 w/o Digital Filter 60
Page 59 and 60: anges from 10 to 20. If 50 percent
Page 61 and 62: Node 1 x1 i =1,2,…,ns E x 1
Page 63 and 64: Correlation function estimate (m 2
Page 65 and 66: Figure 5.5. Effect of data loss and
Page 67 and 68: Frobenius norm. Because only a limi
Page 69 and 70: 90 80 70 60 50 Node1 Node2 Node3 No
Page 71 and 72: On reception of a NACK, the sender
Page 73 and 74: Sender SendLData.send() Pack parame
Page 75 and 76: If there are missing packets, the r
Page 77 and 78: Sender SendLData.bcast() Pack param
Page 79 and 80: Sender Send a packet • Sender sen
Page 81 and 82: 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
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10 0 10 -2 10 -4 0 500 1000 1500 20
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Table 7.1. SVD Accuracy Check on Ma
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7.1.4 Complex matrix inverse A comp
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Figure 7.9. Details of the joint (G
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Figure 7.13. Amplifier (Gao, 2005).
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0.08 0.2 Acceleration (g) 0.04 0 -0
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3 Imote2_2/Imote2_1 Imote2_1/Siglab
Page 125 and 126:
200 3 Phase of spectral densities (
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1.5 x10-17 Time (sec) Difference in
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input forces applied at nodes in th
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CH1 CH2 CH3 …. CHn-1 CHn Initiali
Page 133 and 134:
1 2 Node ID 102 RETAKE 1 3 4 INCONS
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Initialization: within local sensor
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PC Base station Manager sensor Clus
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Page 141 and 142:
Page 143 and 144:
Table 7.7. Instructions in DCS Code
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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 #
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for a SHM strategy does not necessa
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are saved. The 420 seconds listed a
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1 1 Normalized accumulated stress m
Page 159 and 160:
Page 161 and 162:
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
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structural vibration is considered
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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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