On Structural Properties to Improve FMEA-Based Abductive Diagnosis

Abductive Model-Based Diagnosis (MBD) provides
an intuitive approach to fault identification by
reasoning on a description of the system to be diagnosed.
Nevertheless, its computational complexity
hinders a vast adoption and thus motivates further
evaluation of efficient methods. In this paper, we
investigate the structural metrics inherent to models
and diagnosis problems generated on the basis of
Failure Mode Effect Analysis (FMEA). Proceeding
on the metrics developed, we investigate their potential
as classification features to identify the most
suitable diagnosis algorithm for a particular diagnosis
problem. Evaluated on artificial and practical
samples, our approach shows that the classifier
trained on the described metrics is able to indicate
the most efficient method in case of a specific diagnosis
scenario