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Proceedings Paper

Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems
Author(s): Saleh Zein-Sabatto; Maged Mikhail; Mohammad Bodruzzaman; Martin DeSimio; Mark Derriso; Alireza Behbahani
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Paper Abstract

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementation results of the developed fusion algorithms on structural health monitoring data collected from experimental tests are reported in this paper.

Paper Details

Date Published: 10 May 2012
PDF: 10 pages
Proc. SPIE 8407, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012, 84070A (10 May 2012); doi: 10.1117/12.919731
Show Author Affiliations
Saleh Zein-Sabatto, Tennessee State Univ. (United States)
Maged Mikhail, Tennessee State Univ. (United States)
Mohammad Bodruzzaman, Tennessee State Univ. (United States)
Martin DeSimio, Univ. of Dayton Research Institute (United States)
Mark Derriso, Air Force Research Lab. (United States)
Alireza Behbahani, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8407:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012
Jerome J. Braun, Editor(s)

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