Structural Health Monitoring Using Smart Sensors - ideals ...

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Table 2.1. <strong>Smart</strong> Sensor Specifications (Continued) (The power consumption is estimated based on associated components’ data sheet.) Radio Chip CC2420 CC1000 CC1000 CC2420 CC2420 Radio frequency (MHz) 2400-2483.5 315/433/915 315/433/915 2400-2483.5 2400-2483.5 Data rate (kbps) 250 38.4 38.4 250 250 Outdoor range (m) 100 300/300/150 300/300/150 100 100 Transmission power at 1 mW power (mW) 52 31 31 52 52 Reception power (mW) 59 22 22 59 59 Typical battery option 2 x AA 2 x AA CR2354 2 x AA 3 x AAA Typical capacity (mAh) 2 x 1500-2000 2 x 1500-2000 560 2 x 1500-2000 3 x 700 Typical supply voltage 3 3 3 3 4.5 Size (mm) 58 x 32 x 7 58 x 32 x 7 25 x 6 65 x 31 x 6 48 x 36 x 7 Available Sensor MicaZ Mica2 Mica2dot Telos Imote2 Light Temperature Humidity Barometric pressure Accelerometer GPS Microphone Sounder Magnetometer Light Temperature Humidity Barometric pressure Accelerometer GPS Microphone Sounder Magnetometer Light Temperature Accelerometer Light Temperature Humidity Light Temperature Humidity Accelerometer 13
While a number of sensor boards for the Berkeley Mote have been designed for generic applications and are commercially available (see Table 2.1), available sensors are not optimized for use in civil infrastructure applications. Acceleration and strain are among the most important physical quantities to judge the health of a structure. While acceleration measurements are essential to obtain global responses of a structure, structural strains provide an important indicator of local structural behavior. Studer and Peters (2004) demonstrated that multiscale sensing yields better results than single-scale measurements for damage identification. Available sensor boards have accelerometers, though their applicability to civil infrastructure is limited. Ruiz-Sandoval (2004) examined an acceleration sensor board, the MTS310CS, from the civil engineering perspective; he found the accelerometer's performance is deficient, particularly in the low frequency range, and developed a new sensor board, 'Tadeo,' with high sensitivity and low noise level. The sensor boards of the Berkeley Mote can be conveniently customized according to the applications. The Berkeley Mote uses a open-source operating system, TinyOS, which is available online (http://www.tinyos.net). TinyOS is a component-based operating system designed for sensor network applications on resource-constrained hardware platforms, such as the Berkeley Mote. More specifically, it is designed to support concurrency intensive operations such as are required by networked sensors with minimal hardware requirements; TinyOS fits in about 4 kB of memory space. TinyOS is, however, not a realtime operating system. TinyOS has only two levels of priority, each of which corresponds to hardware interrupt and tasks. Users cannot assign priority arbitrarily to specific tasks or assume highly precise scheduling. This limitation needs to be well considered in designing a system using TinyOS. A wide community uses TinyOS to develop and test various algorithms and protocols. The operating system has been ported to over a dozen platforms employing numerous sensor boards. The autonomous characteristics of the smart sensor can be realized by developing programs under TinyOS and then running these programs, along with the operating system, on the on-board microprocessor. Similar software for the Berkeley Mote and other platforms includes MANTIS (Bhatti et al., 2005) and EmStar (Girod et al., 2004). Because of the open nature, numerous researchers have contributed to TinyOS enhancement; users can take advantage of the wealth of previous studies. Open-interface smart sensor platforms developed by companies, such as Ember (2007) and Intel (2007), have similar potential. Users have access to the programming interface and can program the sensor nodes as needed. Ember nodes must be programmed on their own OS, but users can implement necessary functionalities through an open interface. Ember proposed its own algorithm to construct the mesh-network. Though the ready-to-use mesh networking is advantageous for some applications, the patented nature of networking may result in an inflexible network system design. Intel recently developed the Intel Imote2, which runs TinyOS or Linux (Adler et al., 2005). The Imote2 provides enhanced computation and communication capabilities that allow resource-demanding sensor network applications, such as SHM of civil infrastructure, to be supported, while low-power operation and small physical size are still among the objectives. The wealth of studies using TinyOS can be implemented directly to the Imote2; this commercial open- 14
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: Table 2.1. Smart Sensor Specificati
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 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
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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
Page 89 and 90:
1.8715 x10 5 #ofdatablocks 1.871 me
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Before this decimation is applied,
Page 93 and 94:
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
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
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these algorithms are implemented on
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10 0 10 -2 10 -4 0 500 1000 1500 20
Page 113 and 114:
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
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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
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1 2 Node ID 102 RETAKE 1 3 4 INCONS
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Initialization: within local sensor
Page 137 and 138:
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
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8 x10-3 Time (sec) Correlation func
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Table 8.2. Mass Normalization Const
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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
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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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