Standard Article

Data Fusion of Multiple Signals from the Sensor Network

Signal Processing

  1. Zhongqing Su1,
  2. Xiaoming Wang2,
  3. Lin Ye3

Published Online: 15 SEP 2009

DOI: 10.1002/9780470061626.shm056

Encyclopedia of Structural Health Monitoring

Encyclopedia of Structural Health Monitoring

How to Cite

Su, Z., Wang, X. and Ye, L. 2009. Data Fusion of Multiple Signals from the Sensor Network. Encyclopedia of Structural Health Monitoring. .

Author Information

  1. 1

    Hong Kong Polytechnic University, Department of Mechanical Engineering, Kowloon, Hong Kong, China

  2. 2

    Commonwealth Scientific and Industrial Research Organisation, CSIRO Sustainable Ecosystems, Melbourne, VIC, Australia

  3. 3

    University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, Sydney, NSW, Australia

Publication History

  1. Published Online: 15 SEP 2009

Abstract

Data fusion is a multilevel and multifaceted process of combining multiple features extracted from a multitude of spatially distributed independent sources, so as to provide capabilities in automatic detection, classification, and identification. The goal of this article is to introduce the currently prevailing data fusion approaches with the purpose of structural health monitoring (SHM). Emphases are particularly placed on the principle of data fusion, fusion architecture, and major fusion algorithms. Representative case studies for evaluating structural damage by using fusion algorithms including correlation measures (CMs), artificial neural network (ANN), and Bayesian inference (BI) are followed.

Keywords:

  • data fusion;
  • structural health monitoring (SHM);
  • damage identification;
  • correlation;
  • artificial neural network (ANN);
  • Bayesian inference (BI)