Structural Health Monitoring Using Smart Sensors - ideals ...

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ABSTRACT Industrialized nations have a huge investment in the pervasive civil infrastructure on which our lives rely. To properly manage this infrastructure, its condition or serviceability should be reliably assessed. For condition or serviceability assessment, Structural Health Monitoring (SHM) has been considered to provide information on the current state of structures by measuring structural vibration responses and other physical phenomena and conditions. Civil infrastructure is typically large-scale, exhibiting a wide variety of complex behavior; estimation of a structure's state is a challenging task. While SHM has been and still is intensively researched, further efforts are required to provide efficient and effective management of civil infrastructure. Smart sensors, with their on-board computational and communication capabilities, offer new opportunities for SHM. Without the need for power or communication cables, installation cost can be brought down drastically. Smart sensors will help to make monitoring of structures with a dense array of sensors economically practical. Densely installed smart sensors are expected to be rich information sources for SHM. Efforts toward realization of SHM systems using smart sensors, however, have not resulted in full-fledged applications. All efforts to date have encountered difficulties originating from limited resources on smart sensors (e.g., small memory size, small communication throughput, limited speed of the CPU, etc.). To realize an SHM system employing smart sensors, this system needs to be designed considering both the characteristics of the smart sensor and the structures to be monitored. This research addresses issues in smart sensor usages in SHM applications and realizes, for the first time, a scalable and extensible SHM system using smart sensors. The architecture of the proposed SHM is first presented. The Intel Imote2 equipped with an accelerometer sensor board is chosen as the smart sensor platform to demonstrate the efficacy of this architecture. Middleware services such as model-based data aggregation, reliable communication, and synchronized sensing are developed. SHM Algorithms identified as promising for the usage on smart sensor systems are extended to improve practicability and implemented on Imote2s. Careful attention has been paid to integrating these software components so that the system possesses identified desirable features. The damage detection capability and autonomous operation of the developed system are then experimentally verified. The SHM system consisting of ten Imote2s are installed on a scale-model truss. The SHM system monitors the truss in a distributed manner to localize simulated damage. In summary, this report proposes and realizes a scalable and autonomous SHM system using smart sensors. The system is experimentally verified to be effective for damage detection. The autonomous nature of the system is also demonstrated. Successful completion of this research paves the way toward full-fledged SHM systems employing a dense array of smart sensors. The software developed under this research effort is opensource and is available at: http://shm.cs.uiuc.edu/.
Contents Page CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Monitoring of civil infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 SHM using smart sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Overview of report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 CHAPTER 2 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Smart sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Smart sensor’s essential features . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Smart sensors to date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Middleware services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Structural Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 Attempts toward SHM using smart sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.1 Research attempts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4.2 Difficulties in using smart sensors for SHM applications . . . . . . . 23 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 CHAPTER 3 SHM ARCHITECTURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.1 Desirable characteristics of an SHM system employing smart sensors . . . . . 28 3.2 SHM system architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.1 Network system architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2 Smart sensor platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.3 Middleware services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2.4 Damage detection algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 CHAPTER 4 SENSOR BOARD CUSTOMIZATION . . . . . . . . . . . . . . . . . . . . . . . 41 4.1 Strain sensor board development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2 AA filter board development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3 Experimental verification of strain sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 CHAPTER 5 MIDDLEWARE SERVICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1 Data aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1.1 Estimate on data amount in SHM applications . . . . . . . . . . . . . . . . 51 5.1.2 Model-based data aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.1.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Reliable communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2.1 The effects of data loss on SHM applications . . . . . . . . . . . . . . . . 57 5.2.2 Packet loss estimation in RF communication . . . . . . . . . . . . . . . . . 61 5.2.3 Reliable communication protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Page 1 and 2: NSEL Report Series Report No. NSEL-
Page 3: The Newmark Structural Engineering
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
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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
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Sender SendLData.bcast() Pack param
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Sender Send a packet • Sender sen
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utilized in SHM applications follow
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where R xxref is a vector consistin
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Time (sec) 80 60 40 20 0 -20 -40 -6
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Difficulties encountered in realizi
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1.8715 x10 5 #ofdatablocks 1.871 me
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Before this decimation is applied,
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where xn is the original signal,
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Timer firing every T3 110 data poin
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locks before or after the current b
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Chapter 6 ALGORITHMS In this chapte
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The eigenproblem of the system matr
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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
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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
Page 129 and 130:
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
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
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