Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Middleware services for structural health monitoring using smart sensors

  • Nagayama, T. (Department of Civil Engineering, University of Tokyo) ;
  • Spencer, B.F. Jr. (Department of Civil Engineering, University of Illinois at Urbana-Champaign 201 N. Goodwin Ave.) ;
  • Mechitov, K.A. (Department of Computer Science, University of Illinois at Urbana-Champaign 201 N. Goodwin Ave.) ;
  • Agha, G.A. (Department of Computer Science, University of Illinois at Urbana-Champaign 201 N. Goodwin Ave.)
  • Received : 2008.02.01
  • Accepted : 2008.11.12
  • Published : 2009.03.25
⟨ Previous Next ⟩

Abstract

Smart sensors densely distributed over structures can use their computational and wireless communication capabilities to provide rich information for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Hardware resources available on smart sensors restrict data acquisition capabilities; intrinsic to these wireless systems are packet loss, data synchronization errors, and relatively slow communication speeds. This paper addresses these issues under the hardware limitation by developing corresponding middleware services. The reliable communication service requires only a few acknowledgement packets to compensate for packet loss. The synchronized sensing service employs a resampling approach leaving the need for strict control of sensing timing. The data aggregation service makes use of application specific knowledge and distributed computing to suppress data transfer requirements. These middleware services are implemented on the Imote2 smart sensor platform, and their efficacy demonstrated experimentally.

Keywords

Acknowledgement

Supported by : National Science Foundation

References

  1. Bendat, J. S. and Piersol, A. G. (2000), Random data: analysis and measurement procedures, New York: Wiley.
  2. Crossbow Technology, Inc.
  3. Elson, J., Girod, L. and Estrin, D. (2002), "Fine-grained network time synchronization using reference broadcasts", Proceedings of 5th Symposium on Operating Systems Design and Implementation, Boston, MA.
  4. Ganeriwal, S., Kumar, R. and Srivastava, M. B. (2003), "Timing-sync protocol for sensor networks", Proceedings of 1st International Conference On Embedded Networked Sensor Systems, Los Angeles, CA., 138-149.
  5. Gao, Y. (2005), "Structural health monitoring strategies for smart sensor networks", Doctoral dissertation, University of Illinois at Urbana-Champaign, 2005.
  6. James, G. H., Carne, T. G. and Lauffer, J. P. (1993), "The natural excitation technique for modal parameter extraction from operating wind turbine", Report No. SAND92-1666, UC-261, Sandia National Laboratories, NM.
  7. Kim, S., Pakzad, S., Culler D., Demmel, J., Fenves, G., Glaser, S. and Turon, M. (2007), "Health monitoring of civil infrastructures using wireless sensor networks", Proceedings of the 6th international conference on Information processing in sensor networks, Cambridge, MA, 254-263.
  8. Lamport, L., Shostak, R. and Pease, M. (1982), "The Byzantine Generals Problem", ACM Transactions on Programming Languages and Systems, 4(3), 382-401. https://doi.org/10.1145/357172.357176
  9. Lynch, J. P. and Loh, K. (2006), "A summary review of wireless sensors and sensor networks for structural health monitoring", Shock Vib. Digest, 38(2), 91-128. https://doi.org/10.1177/0583102406061499
  10. Maroti, M. Kusy, B., Simon, G. and Ledeczi, A. (2004). "The flooding time synchronization protocol", Proceedings of 2nd International Conference On Embedded Networked Sensor Systems, Baltimore, MD, 39-49.
  11. Mechitov, K. A., Kim, W., Agha, G. A. and Nagayama, T. (2004), "High-frequency distributed sensing for structure monitoring", Proceedings of 1st Int. Workshop on Networked Sensing Systems, Tokyo, Japan, 101-105.
  12. Nagayama, T., Rice, J. A. and Spencer, B. F., Jr. (2006a), "Efficacy of Intel's Imote2 wireless sensor platform for structural health monitoring applications", Proceedings of Asia-Pacific Workshop on Structural Health Monitoring, Yokohama, Japan.
  13. Nagayama, T. Sim, S.-H., Miyamori, Y. and Spencer, B. F. Jr. (2007a), "Issues in structural health monitoring employing smart sensors", Smart Struct. Sys., 3(3), 299-320. https://doi.org/10.12989/sss.2007.3.3.299
  14. Nagayama, T. and Spencer, B. F. Jr. (2007), "Structural health monitoring using smart sensors", Newmark Structural Engineering Laboratory Report Series 001 http://hdl.handle.net/2142/3521.
  15. Nagayama, T., Spencer, B. F., Jr., Agha, G. A. and Mechitov, K. A. (2006b), "Model-based data aggregation for structural monitoring employing smart sensors", Proceedings of 3rd Int. Conference on Networked Sensing Systems (INSS 2006), Chicago, IL, 203-210.
  16. Nagayama, T., Spencer, B. F. Jr. and Fujino, Y. (2007b), "Synchronized sensing toward structural health monitoring using smart sensors", Proceedings of World Forum on Smart Materials and Smart Structures Technology (SMSST 2007), Chongqing & Nanjing, China.
  17. Oppenheim, A. V., Schafer, R. W. and Buck, J. R. (1999), Discrete-Time Signal Processing, Upper Saddle River, NJ: Prentice Hall.
  18. Spencer, B. F., Jr. and Nagayama, T. (2006), "Smart sensor technology: A new paradigm for structural health monitoring", Proceedings of Asia-Pacific Workshop on Structural health Monitoring, Yokohama, Japan.
  19. STMicroelectronics. .

Cited by

  1. A Recent Research Summary on Smart Sensors for Structural Health Monitoring vol.19, pp.3, 2015, https://doi.org/10.11112/jksmi.2015.19.3.010
  2. Vehicle Model Calibration in the Frequency Domain and its Application to Large-Scale IRI Estimation vol.12, pp.3, 2017, https://doi.org/10.20965/jdr.2017.p0446
  3. Effects of Wireless Sensor Network Uncertainties on Output-Only Modal Analysis Employing Merged Data of Multiple Tests vol.17, pp.3, 2014, https://doi.org/10.1260/1369-4332.17.3.319
  4. Investigations optimum scheduling and TCP mechanism of hybrid topology sensor network in building SHM vol.127, pp.6, 2016, https://doi.org/10.1016/j.ijleo.2015.11.118
  5. Low-Latency Data Acquisition Hardware for Real-Time Wireless Sensor Applications vol.15, pp.3, 2015, https://doi.org/10.1109/JSEN.2014.2366932
  6. Autonomous decentralized structural health monitoring using smart sensors 2009, https://doi.org/10.1002/stc.352
  7. Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors vol.33, pp.7, 2011, https://doi.org/10.1016/j.engstruct.2011.03.007
  8. Enabling framework for structural health monitoring using smart sensors vol.18, pp.5, 2011, https://doi.org/10.1002/stc.386
  9. Identification of moving vehicle parameters using bridge responses and estimated bridge pavement roughness vol.153, 2017, https://doi.org/10.1016/j.engstruct.2017.10.006
  10. The principle and optimal design of a cochlea-inspired artificial filter bank (CAFB) for structural health monitoring vol.21, pp.1, 2017, https://doi.org/10.1007/s12205-016-0431-7
  11. TinyOS-based real-time wireless data acquisition framework for structural health monitoring and control vol.20, pp.6, 2013, https://doi.org/10.1002/stc.1514
  12. Recent advances in wireless smart sensors for multi-scale monitoring and control of civil infrastructure vol.6, pp.1, 2016, https://doi.org/10.1007/s13349-015-0111-1
  13. A Study on the Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of Civil Structures vol.17, pp.7, 2017, https://doi.org/10.3390/s17071620
  14. An Experimental Study of a Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System for Structural Health Monitoring of a Long-Span Bridge vol.8, pp.3, 2018, https://doi.org/10.3390/app8030361
  15. Development of a Wireless Unified-Maintenance System for the Structural Health Monitoring of Civil Structures vol.18, pp.5, 2018, https://doi.org/10.3390/s18051485
  16. Wireless sensor networks for long-term structural health monitoring vol.6, pp.3, 2009, https://doi.org/10.12989/sss.2010.6.3.263
  17. Output-only modal identification approach for time-unsynchronized signals from decentralized wireless sensor network for linear structural systems vol.7, pp.1, 2011, https://doi.org/10.12989/sss.2011.7.1.059
  18. Extraction of bridge fundamental frequency from estimated vehicle excitation through a particle filter approach vol.428, pp.None, 2009, https://doi.org/10.1016/j.jsv.2018.04.030
  19. Road profile estimation, and its numerical and experimental validation, by smartphone measurement of the dynamic responses of an ordinary vehicle vol.457, pp.None, 2009, https://doi.org/10.1016/j.jsv.2019.05.015
  20. Road profile estimation and half-car model identification through the automated processing of smartphone data vol.142, pp.None, 2009, https://doi.org/10.1016/j.ymssp.2020.106722
  21. Compressed sensing-based electromechanical admittance data loss recovery for concrete structural health monitoring vol.20, pp.3, 2009, https://doi.org/10.1177/1475921720950640