Data Acquisition, Detection and Estimation for Structural Health Monitoring

Although using sensor networks for SHM (structural health monitoring) is not a new concept, very few projects have investigated the problems of detection (of defects) and estimation (of damage location) using network-acquired data. In statistics detection and estimation theory were established by assuming the measurement data come with reliable statistics, for instance, the probability of a particular observation. However, such statistics often requires large amount of observations. In wireless sensor networks, data acquisition is a costly operation since wireless sensor networks are both bandwidth and power limited. The amount of measurement data that can be reported to the base station is therefore very limited. Data acquisition from sensor networks has been treated as a trivial subject and often is performed by using fixed-interval sensing and reporting. In this project, we will provide a thorough treatment of sampling, detection and estimation for using sensor network data. Specifically, (a) We will investigate the fundamental sampling issue, particularly, for each type of physical measurement, what is the best sampling rate and whether adaptive sampling is more suitable than uniform sampling. Based on the sampling discipline, the sensing and communication protocols are developed; (2) for structural defect detection, we propose to use the likelihood ratio test method with Bayes criterion and compare it with the basic LRT method; through the detector, we narrow the scope of the defect to be within the spatial interval of some sampling points; (3) once it is concluded that a defect exists, the maximum likelihood estimator is used to further estimate the location of the defect. The algorithms will be validated thorough test bed experiments or simulations.


  • English


  • Status: Completed
  • Funding: $16052.00
  • Contract Numbers:



  • Sponsor Organizations:

    Missouri University of Science & Technology, Rolla

    Department of Engineering
    202 University Center
    Rolla, MO    65409
  • Principal Investigators:

    Cheng, Maggie

  • Start Date: 20130515
  • Expected Completion Date: 0
  • Actual Completion Date: 20131231
  • Source Data: RiP Project 34552

Subject/Index Terms

Filing Info

  • Accession Number: 01530035
  • Record Type: Research project
  • Source Agency: Center for Infrastructure Engineering Studies
  • Contract Numbers: DTRT06-G-0014, 00042509
  • Files: UTC, RIP
  • Created Date: Jul 3 2014 1:01AM