Structural Health Monitoring Using Smart Sensors - ideals ...

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When smart sensor applications involve more and more complicated internode data processing and are assigned more and more tasks by commands sent through packets, these commands needs to be delivered reliably. Reliable communication of short messages is clearly a significant help in developing SHM systems with complicated internode data processing. The need to transfer a large amount of data reliably is not necessarily clear. In many SHM research efforts, the data loss problem is not addressed. Loss of a few data points has often been considered acceptable. However, the rationale behind accepting a small packet loss rate is not clear. In section 5.2.1, the effects of data loss on SHM applications are assessed. The packet loss rate of the Imote2 is then experimentally examined. The packet loss rate varies from experiment to experiment. An experiment with nodes close to each other is expected to have less packet loss than an experiment with sparsely distributed nodes. Packet loss rate is estimated under several conditions in section 5.2.2 Subsequently, reliable communication protocols suitable for sending large amounts of data, as well as protocols to send single packets, are proposed. 5.2.1 The effects of data loss on SHM applications In a wireless network, some packets are inevitably lost during communication unless the communication protocol is specifically designed for reliable communication. Conventional statistical, modal, and structural analyses of structural response data, however, assume that no loss of data takes place. Some researchers have been working to develop reliable communication without data loss, while others just ignore the data loss effects on their analyses. Modal analysis and damage localization has not yet been examined from the perspective of data loss. The impact of data loss on SHM applications is investigated herein. The Distributed Computing Strategy (DCS) for SHM described in section 6.4 is used as a benchmark application. The correlation function and impulse response function estimation, as well as modal analysis, employed as a part of DCS are widely used to analyze ambient vibration data of civil infrastructure. The outcome of this data loss analysis is applicable to many vibration-based SHM applications. The damage detection method adapted in DCS is the DLV method, and the effect of data loss on this damage detection method is also investigated. Understanding the effect of data loss may provide insight into how to accommodate communication with data loss that is less demanding on resource limited smart sensors than the communication without data loss (Nagayama et al., 2007). A computer simulation study is conducted for a truss model (see Figure 5.3), assuming various data loss levels to investigate the data loss effect. <strong>Smart</strong> sensors are assumed to be placed at the 13 nodes on the lower chord to measure the vertical acceleration. The vertical input excitation at node 11 is measured. The sampling frequency is set at 380 Hz so that the Nyquist frequency is above the fourth natural frequency of the structure. After the data is acquired, a certain percentage of the data is randomly dropped 57
Figure 5.5. Effect of data loss and observation noise: acceleration power spectral density. to simulate data loss. Data loss is assumed to take place only at this step, where the largest amount of data is transferred among the sensor nodes. Data is processed following each step of DCS. The impulse response functions associated with each measurement points are estimated. Then the impulse response functions are fed into the ERA routine for modal analysis. The natural frequency, damping ratio, and mode shapes are among the major outputs. These modal parameters yield flexibility matrix estimations. Then, one of the truss elements of the structure, element 9, is replaced with a damaged element, which has a 40 percent cross-section reduction. Data acquisition, impulse response estimation, modal analysis, and flexibility matrix estimation are repeated on this data. From the estimated flexibility matrices for an undamaged and damaged model, the DLVs are calculated. The accumulated stress, small values of which indicate possible damaged elements, is then calculated. This simulation is conducted assuming several data loss levels. For the results of the data loss analysis to be better interpreted, the outcome is compared to that of computer simulation including observation noise (but without data loss). A band-limited white noise is superimposed onto each of observed signals. RMS noise level is specified as a percentage of the RMS of physical responses. The effect of data loss is first examined by comparing the PSD and coherence functions of the measurement data. Figure 5.5 shows representative PSD functions calculated with and without noise/data loss. The PSD’s peaks remain nearly unchanged when the data loss or the noise is introduced; however, zeros are blurred. Although further investigation is necessary for quantitative judgment, these results indicate that data loss of 0.5 percent and 5 percent observation noise have similar impact on PSD estimation. Coherence functions indicate the degree of linearity between two variables. In this computer simulation, the input excitation is applied only at node 11. Because the response of the truss structure is linear, the coherence function between the two measurement signals is expected to be unity over the entire frequency range. When no data loss and no observation noise are considered, the coherence function is indeed close to one, as shown 58
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NSEL Report Series Report No. NSEL-
Page 3 and 4:
The Newmark Structural Engineering
Page 5 and 6:
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
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: Correlation function estimate (m 2
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
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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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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.
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Yi, J.-H. & Yun, C.-B. (2004). “C
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