Structural Health Monitoring Using Smart Sensors - ideals ...

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Table 3.1. Difficulties in SHM Using Smart Sensors and Approaches in This Report Difficulty Sensor hardware Data aggregation Time synchronization Limited computational capability Power Approach • The Imote2 sensor board, which has a triaxial accelerometer, is employed. The performance of the accelerometer is compared with reference sensors. • Sensor board customizability is demonstrated. • Model-based data aggregation is proposed. • DCS for SHM allows distributed data processing, eliminating the need for centrally collecting a large amount of data. • Synchronized sensing middleware service is proposed. • Computational tasks are distributed by employing model-based data aggregation and DCS for SHM. • The Imote2 has a relatively fast microprocessor as well as large RAM. • Power consumption is kept moderate by employing DCS for SHM and model-based data aggregation. • CPU clock frequency is switched, depending on tasks to perform. • Power harvesting is not addressed in this research. 37
Table 3.2. Desirable Characteristics and Approaches in This Report Desirable characteristic Scalable resource aware system Scalability Power awareness Bandwidth awareness Memory awareness Computational power awareness Modal operation Autonomous distributed embedded computing Model-based data aggregation Collaborative distributed data processing Approach • DCS for SHM and model-based data aggregation offers scalability. • RF and flash memory usage are kept moderate through DCS for SHM and model-based data aggregation, as well as implementation ingenuity. • The homogeneous hardware configuration of the network potentially offers reassignment of roles based on available power. • RF communication is managed by manager node and cluster heads. • Reliable communication protocols, which wait for longer intervals in a congested communication network, are developed. • SHM system is designed so that requests for memory usage do not exceed the currently available memory size of the Imote2, that is 256 kB. • Computational tasks are distributed by employing DCS for SHM and modelbased data aggregation. • The frequency scalable processor on the Imote2 is utilized to make efficient use of computational power. • Smart sensors get ready to enter sleep mode. • TinyOS that supports modal operation is employed. • Model-based data aggregation is proposed and implemented. • DCS for SHM and model-based data aggregation realize collaborative, distributed data processing. 38
Page 1 and 2: 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: commands; execution timing cannot b
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 and 56: Strain ( ) 80 w/o Digital Filter 60
Page 57 and 58: performance was experimentally vali
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
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 139 and 140:
Page 141 and 142:
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 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
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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