Structural Health Monitoring Using Smart Sensors - ideals ...

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modes that have natural frequencies close to these peaks as the physical modes. ERA is, thus, implemented on the Imote2 having limited memory. The accuracy of the developed ERA routine is now numerically examined. ERA is applied to the correlation functions estimated in section 7.2.2. The Imote2 corresponding to node 4 performs ERA and identifies the natural frequencies, damping ratios, mode shapes, modal amplitude coherences, and initial modal amplitudes. Natural frequencies, damping ratios, and mode shapes are compared with those calculated on the PC and are summarized in Table 7.3. The error in the frequency and damping ratio estimates is calculated as the difference between the estimates on the Imote2 and the PC. The estimation error in the mode shapes are investigated in terms of the Modal Assurance Criterion (MAC) defined in Eq. (7.1). As is seen in Table 7.3, the modal identification results on the Imote2 and those on the PC are identical with the precision of double data type. Table 7.3. Identification of Natural Frequencies, Damping Ratios and Mode Shapes mode Natural Frequency Damping Ratio Mode Shape f (Hz) difference (%) ( – ) difference 1-MAC ( ) ( ) 1 20.1526 5.3078 3.5523 2.3794 2.2204 2 41.2039 -0.4263 0.3439 0.1252 0 3 61.9044 -0.1137 0.3812 -0.0944 0 4 66.9313 -0.0142 0.5763 -0.0648 -2.2204 5 70.4128 -0.5826 0.7909 -0.9630 2.2204 6 93.8481 -0.0426 0.3118 0.7626 -2.2204 7.2.4 DLV methods Implementation of the DLV methods on the Imote2 is described in this section. These implementations are validated through comparison between the results on the Imote2 and the PC. Deliberate consideration is given to the implementation of the DLV methods to accommodate the limited hardware resources on the Imote2. One of the numerical operations requiring a large amount of memory and CPU time is stress analysis under the DLV loading. If an FEM model of the entire structure needs to be stored on a smart sensor node, the model may exceed available memory. Numerical operations involved in the analysis of the FEM model, i.e., calculation of static displacements and stresses under the DLV loading, are not trivial. Instead, the linear nature of the analysis is used to simplify the process. A matrix to convert input force to stress is calculated in advance and injected to the cluster heads; rather than to run the entire structural analysis, the cluster heads simply need to compute the product of the matrix and DLVs. Furthermore, the conversion matrix needs to keep only the submatrix corresponding to the structural node and elements in the local sensor community corresponding to the cluster head. The submatrix converts 121
input forces applied at nodes in the local sensor community to stress in elements in the same community. The structural analysis is, thus, performed as the product of the modest size matrix and DLVs The DLV method implementation on the Imote2 is now numerically examined. The DLV method based on a known mass perturbation is employed in this numerical validation; a similar procedure was employed to implement and validate the SDLV method. Prior to monitoring local sensor communities, mass normalization constants are estimated. These constants are also estimated on the Imote2. The structural responses of the truss before and after the addition of the known mass at node 11 are injected to 10 Imote2s corresponding to nodes 7, 9, 11, 13, 15, 17, 19, 21, 23, and 25. The Imote2 at node 21 works as the cluster head and applies NExT and ERA before and after the mass perturbation. <strong>Using</strong> the identified modal parameters, the cluster head estimates the mass normalization constants using Eq. (6.21). The estimated mass normalization constants are listed in Table 7.4 together with the difference between the constants estimated on the Imote2 and the PC. The mass normalization constant estimation on the Imote2 is numerically identical to that on the PC. <strong>Monitoring</strong> of the local sensor community consisting of six Imote2s at nodes 2, 3, 4, 5, 6, and 7 is now examined. The injection of data, correlation function estimates by NExT, and modal identification by ERA are repeated for the truss acceleration response data measured before and after element 9 is replaced with a thinner element. Identified modal frequencies and mode shapes before and after damage, as well as the estimated mass normalization constants, are used to construct flexibility matrices. As intermediate results, the singular values in Eq. (6.24) estimated on the Imote2 and on the PC are compared in Table 7.5. The DLVs calculated on the Imote2 are also compared with those estimated on the PC. The singular values and DLVs are considered numerically identical. The DLVs are then applied to the truss model to estimate the normalized accumulated stress. As shown in Table 7.6, the stress in element 9 is smaller than the threshold value of 0.3. Because the normalized accumulated stress is only compared with the threshold value, the stress does not need to have many effective bits. Therefore, the stress estimation on the Imote2 is performed in a single-precision float data type having approximately seven effective digits. The normalized accumulated stress estimated on the Imote2 and on the PC is identical with respect to the precision of the float data type. 7.2.5 DCS logic The DCS logic to find inconsistency in damage localization results is implemented on the Imote2. Sensor communities close to each other exchange information about damaged elements. Because the topology of sensor communities is formulated to have overlaps in monitored elements, at least two sensor communities monitor each element of the structure. If some communities find contradicting damage detection results, these communities repeat sensing, data processing, and the DCS logic. Also, if sensor communities detect damaged elements in a consistent manner, these communities report 122
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NSEL Report Series Report No. NSEL-
Page 3 and 4:
The Newmark Structural Engineering
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Contents Page CHAPTER 1 INTRODUCTIO
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9.2.3 Power harvesting . . . . . .
Page 9 and 10:
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
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: 1.5 x10-17 Time (sec) Difference in
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 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 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
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Sazonov, E., Janoyan, K., & Jha, R.
Page 185 and 186:
Yi, J.-H. & Yun, C.-B. (2004). “C
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