Structural Health Monitoring Using Smart Sensors - ideals ...

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(a) Figure 5.6. Effect of data loss and observation noise: coherence functions between (a) nodes 11 and 19; and (b) nodes 19 and 25. in Figure 5.6. The isolated downswings correspond to zeroes of the system; extremely small responses of the structure at these frequencies at the zeros result in numerical error, giving small coherence function. The loss of 0.5 percent of data yields a much smaller coherence function, except at the system’s poles (see Figure 5.6). Because of the random nature of data loss occurrence and the excitation, the coherence function varies from simulation to simulation. Twenty time average of the coherence functions are shown in this figure. The coherence function’s discrepancy from unity depends on the two measurement nodes between which the function is calculated. All of the investigated coherence function plots, however, support that the loss of 0.5 percent of data affects the coherence function in a similar way as 5 to 10 percent measurement noise addition. Though the data loss clearly impacts the PSD estimation and coherence function negatively, the consequence in subsequent modal analyses is unclear. The modal properties are mainly represented by frequency components near the natural frequency. The PSD and coherence function are not affected by data loss and noise as much near the poles. The ERA modal analysis method is then applied to the impulse response functions to estimate the modal properties. Impulse response functions are the inverse Fourier transform of the transfer functions from the input force to measured outputs, which is calculated from CSD and PSDs. Figure 5.7 shows how the estimates of the first natural frequency and the damping ratio vary as a function of the data loss level. For comparison, these modal parameters are also estimated from the simulation model subjected to measurement noise (see Figure 5.8). Again, a loss of 0.5 percent of data is approximately equivalent to 5 to 10 percent measurement noise. The first four modes are identified and these modal properties are used to estimate the flexibility matrix F . The flexibility matrix estimate Fˆ is compared with F and the estimation error is shown in Figure 5.9 in terms of the Frobenius norm. represents the 59 (b) F
Frobenius norm. Because only a limited number of modes are utilized in the flexibility matrix estimation, the Frobenius norms do not approach zero at 0 percent data loss and 0 26.026 0.66 1st Natural Frequency (Hz) 26.024 26.022 26.02 26.018 26.016 26.014 mean mean ± RMS 26.012 0 0.2 0.4 0.6 0.8 1 Data loss (%) (a) 1st Damping Ratio (%) 0.65 0.64 0.63 0.62 0.61 0.6 mean mean ± RMS 0 0.2 0.4 0.6 0.8 1 Data loss (%) Figure 5.7. Data loss dependency of (a) the 1st natural frequency; and (b) damping ratio. (b) 26.026 0.66 1st Natural Frequency (Hz) 26.024 26.022 26.02 26.018 26.016 26.014 mean mean ± RMS 26.012 0 2 4 6 8 10 Noise Level (%) 1st Damping Ratio (%) 0.65 0.64 0.63 0.62 0.61 0.6 mean mean ± RMS 0 2 4 6 8 10 Noise Level (%) (a) (b) Figure 5.8. Noise level dependency of (a) the1st natural frequency; and (b) damping ratio ||F-F0||F/||F0||F 0.11 0.1 0.09 0.08 0.07 mean mean ± RMS 0.06 0 0.2 0.4 0.6 Data loss (%) 0.8 1 60 Normalized accumulated stress 0.2 0.15 0.1 0.05 0 mean mean ± RMS 0 0.2 0.4 0.6 0.8 1 Data loss (%) (a) (b) Figure 5.9. Data loss dependency of (a) flexibility matrix estimation error; and (b) normalized accumulated stress in the damaged element.
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NSEL Report Series Report No. NSEL-
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The Newmark Structural Engineering
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Contents Page CHAPTER 1 INTRODUCTIO
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9.2.3 Power harvesting . . . . . .
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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
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 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: Figure 5.5. Effect of data loss and
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
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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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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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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). “
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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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