A new damage detection and tracking method using smart sensor network

被引:5
作者
Jalalpour M. [1 ]
Azarbayejani M. [2 ]
El-Osery A.I. [3 ]
Reda Taha M.M. [4 ]
机构
[1] Structural Technologies, LLC, Hanover, MD
[2] Department of Civil Engineering, University of Texas-Pan American, Texas
[3] Department of Electrical Engineering, New Mexico Technology, Socorro, NM
[4] Department of Civil Engineering, University of New Mexico, Albuquerque, NM
关键词
Damage tracking; Sensor networks; Smart structures; Structural health monitoring;
D O I
10.1007/s13349-016-0167-6
中图分类号
学科分类号
摘要
Large sensor networks have been proposed as part of structural health monitoring (SHM) systems for infrastructure safety. To apply SHM on large structures such as pipelines, a considerably large number of sensors are required. By increasing the number of sensors, the amount of data to communicate and analyze becomes a burden due to the required computational overhead, power and communication cost. In this paper, a new methodology for detection and tracking capable of minimizing the necessary collected data without compromising damage detection and tracking is presented. Our novel approach combines damage feature correlation and a probabilistic on/off scheme to minimize the required number of activated sensors for damage detection. The amount of preprocessing data to select the on sensors compared to the overall processing is considerably small. Consequently, the new approach minimizes power demand for limiting the amount of data being communicated and further promoting the use of wireless technologies. The randomness of the process leads to an efficient damage tracking method due to minimizing the overall cost of the system. A case study of corrosion damage detection and tracking in a steel pipeline is presented and discussed. It is shown that the proposed method enables successful damage detection and tracking with less than 25 % of the total installed sensors at any time of operation. © 2016, Springer-Verlag Berlin Heidelberg.
引用
收藏
页码:291 / 301
页数:10
相关论文
共 17 条
  • [1] Azarbayejani M., Jalalpour M., El-Osery A.I., Reda Taha M.M., Field application of smart SHM using field programmable gate array (FPGA) technology to monitor an RC bridge in New Mexico, J Smart Mater Struct, 20, 8, (2011)
  • [2] Azarbayejani M., Reda Taha M.M., Ross T.J., An inductive fuzzy damage classification approach for structural health monitoring, Int J Mater Struct Integr, 2, 3, pp. 193-206, (2008)
  • [3] Azarbayejani M., El-Osery A., Choi K.K., Reda Taha M.M., A probabilistic approach for optimal sensor allocation in structural health monitoring, Smart Mater Struct ASCE, 17, pp. 55019-55029, (2008)
  • [4] Buczak A.L., Wang H.H., Darabi H., Jafari M.A., Genetic algorithm convergences study for sensor network optimization, Inf Sci, 133, pp. 267-282, (2001)
  • [5] Chakrabarty K., Chiu P.K., Grid coverage for surveillance and target location in distributed sensor networks, IEEE Trans Comput, 51, pp. 1448-1453, (2002)
  • [6] Chang F.K., Markmiller J.F.C., A new look in design of intelligent structures with SHM. In: Proceedings of the 3rd European workshop: structural health monitoring, pp 5–20, (2006)
  • [7] Chang F.K., Markmiller J.F.C., Ihn J.B., Cheng K.Y., A potential link from damage diagnostics to health prognostics of composites through built-in sensors, J Vib Acoust, 129, pp. 718-729, (2007)
  • [8] Farrar C., Cornwell P., Doebling S., Prime M., Monitoring studies of the alamosa canyon and I-40 bridges, (2000)
  • [9] Field R.V.J., Grogoriu M., Optimal design of sensor networks for vehicle detection, classification and monitoring, Probab Eng Mech, 21, pp. 305-316, (2006)
  • [10] Guratzsch R.F., Mahadevan S., Sensor placement design for SHM under uncertainty. In: Proceedings of the 3rd European workshop: structural health monitoring, pp 1168–75, (2006)