Structural Health Monitoring Using Smart Sensors - ideals ...

More documents

Recommendations

Info

1 Correlation Coefficient 0 -1 -4.8 -4.7 -4.6 -4.5 -4.4 Time Shift (sec) Figure 7.14. Correlation coefficients plotted against time shift. keeping the stopband cutoff frequency lower than the Nyquist frequency and completely eliminating the aliasing components, the cutoff frequency is set so that the aliasing components are above 100 Hz. For example, when sampling frequency is converted from 560 to 280 Hz with the passband cutoff frequency of 100 Hz, the stopband cutoff frequency is set to 180 Hz instead of 140 Hz. Note that the actual sampling frequency of the sensor board may have 10 percent variation and is not exactly 560 Hz. The stopband cutoff frequency normalized to the upsampled sampling rate is set to be smaller than 180/ 560L, allowing the variation in the denominator. L is the upsampling factor. Likewise, the normalized passband cutoff frequency is set to be larger than 100/560L. In this way, a filter allowing aliased components outside of the frequency range of interest is designed with fewer filter coefficients than a filter completely eliminating aliased components. The length of the measured data is subject to the RAM size limitation. The sensing function on the Imote2 first stores data in the on-board RAM; the size of RAM limits the sensing duration. For example, a three-axis acceleration measurement using a 16-bit data type for each axis and having 11,264 data points occupies about 67 kB of RAM. Because sensing is first performed at a higher sampling frequency and then downsampled, the RAM originally occupied by the measured acceleration data is about 134 kB. 256 kB of RAM on the Imote2 does not allow continuous measurement of acceleration for an indefinite amount of time. Once the memory space becomes full, sensing is stopped, the stored data is copied to Flash memory, and sensing can be started again. Acceleration signals from the Siglab spectrum analyzer and Imote2s are compared with each other to examine the sensing capability of the Imote2. Because the sampling frequency of the reference signals is different from that of Imote2 signals, which is set at 280 Hz, the reference signals are resampled to 280 Hz. The resampling algorithm described in section 5.3 is utilized. Also, the Siglab system is not synchronized with Imote2s, making direct comparison of the two time domain signals difficult. Signals from the two data acquisition systems are synchronized using the resampling algorithm discussed previously. To estimate the offset, the reference sensor signals are shifted on a time axis by a specified offset and the correlation coefficient between the two signals is calculated. Figure 7.14 provides a plot of the correlation coefficient against the time shift. The offset between the two sets of clocks is determined as the time shift giving the 113
0.08 0.2 Acceleration (g) 0.04 0 -0.04 Imote2 1 Imote2 2 -0.08 Reference 1 Reference 2 10 10.02 10.04 10.06 10.08 10.1 Time (sec) 0.4 0.2 Imote2 1 Imote2 2 Reference 1 Reference 2 (a) longitudinal Acceleration (g) 0.1 0 -0.1 Imote2 1 Imote2 2 Reference 1 Reference 2 -0.2 10 10.02 10.04 10.06 10.08 10.1 Time (sec) (b) transverse Acceleration (g) 0 -0.2 10 10.02 10.04 10.06 10.08 10.1 Time (sec) (c) vertical Figure 7.15. Acceleration record from Imote2s and reference sensors. maximum absolute correlation coefficient. Note that the time shift is not restricted to multiples of the sampling period. The resampling algorithm with linear interpolation allows noninteger multiple of the sampling period as a time shift. Synchronized signals are then compared in time and frequency domains. The measured signals from the Imote2s and PCB accelerometers show reasonable agreement in the time domain. Figure 7.15 shows a comparison of the four time histories. Two of them are from Imote2s and the other two are from the PCB accelerometers. The vertical acceleration signals from Imote2s and PCB accelerometers are almost identical, indicating high sensing performance of Imote2s. Transverse acceleration, however, is not as good as the vertical acceleration. Signals from PCB accelerometers and Imote2s have an observable difference, while signals from the same systems are close to each other. Longitudinal acceleration shows noticeable differences among the four signals. Here, the two signals from the PCB accelerometers show differences while the Imote2 signals appear to be the same. The noise on Imote2 signals specific to each node is considered small. 114
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 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: Figure 7.13. Amplifier (Gao, 2005).
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 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
Page 183 and 184:
Sazonov, E., Janoyan, K., & Jha, R.
Page 185 and 186:
Yi, J.-H. & Yun, C.-B. (2004). “C
show all

Structural Health Monitoring Using Smart Sensors - ideals ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?