|
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.
|
