Structural Health Monitoring Using Smart Sensors - ideals ...

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SENSOR BOARD CUSTOMIZATION Chapter 4 The lack of appropriate sensors and sensing components has hindered progress for SHM applications with smart sensors. In this chapter, the customizability of sensor boards is demonstrated. In particular, a strain sensor board and an AA-filter board will be designed, constructed, and tested. Here, the Berkeley Mote, instead of the Imote2, is chosen as the smart sensor platform because of its availability during the early stages of this research. The customization procedure for the Imote2 is the same as that for the Berkeley Mote. Also, the Berkeley Mote is designed for generic applications, while the Imote2 has been developed for more data intensive applications. Development of a Berkeley Mote sensor board demonstrates that a general-purpose sensor board can be customized for SHM applications. The Berkeley Mote was designed for generic applications; available sensors have not been optimized for use in civil infrastructure applications. 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. However, commercially available sensor boards only have accelerometers for dynamic structural response measurement, and their applicability to civil infrastructure is limited (Ruiz-Sandoval et al., 2006). Although a great variety of strain sensors exist, strain sensor boards have yet to be developed for the Berkeley Mote platform. The search for, selection of, and testing of an appropriate strain sensor tailored to the Mote platform are presented herein. An AA filter board to properly condition the signal from the strain sensor board to the Mica2’s ADC is also designed, constructed, and tested. Experimental results demonstrate that the strain sensor board has good resolution, and that the output is comparable with conventional wired strain sensors. A limitation in the Mica2’s hardware was identified during the experimental verification - a systemic fluctuation was observed in the acquired data during reading/ writing of data by the Mica2’s microprocessor. This problem is addressed through careful design of the analog circuit and subsequent digital signal processing. 4.1 Strain sensor board development Many different types of strain sensors exist that use diverse methods for measurement, such as mechanical, optical, or electrical means. Mechanical strain sensors, for example a slide caliper, are simple but generally have poor resolution. A device with levers to amplify strain to readable values can be devised, but it is prohibitively large. Optical sensors are typically costly and too delicate to be densely deployed on civil infrastructure. Electrical sensors, which include the piezoelectric sensor, semiconductor 41
Figure 4.1. Foil strain gage. strain gage, and the widely used foil strain gage (see Figure 4.1), have high resolution and are small, sturdy, and inexpensive. Thus, they are good candidates for developing a wireless strain sensor. Several desirable characteristics for a smart wireless strain sensors for civil infrastructure applications are presented in the following paragraphs along with a discussion of the associated impact on the design. First, low-frequency responses (e.g.,
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 and 44: commands; execution timing cannot b
Page 45 and 46: Table 3.2. Desirable Characteristic
Page 47: Table 3.2. Desirable Characteristic
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
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same community, eliminating the nee
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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
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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
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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
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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
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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
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Page 161 and 162:
Page 163 and 164:
Table 8.5. Summary of False Damage
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Table 8.7. Summary of False Damage
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e scalability, autonomous distribut
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The lowest frequencies of interest
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structural vibration is considered
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REFERENCES Actis, R. L. & Dimarogon
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Casciati, F., Faravelli, L, Borghet
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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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