  This paper investigates the use of artificial neural networks (ANNs) to
replace traditional algorithms and manual review for identifying anomalies in
vehicle run data. The specific data used for this study is from undersea
vehicle qualification tests. Such data is highly non-linear, therefore
traditional algorithms are not adequate and manual review is time consuming. By
using ANNs to predict nominal vehicle performance based solely on information
available pre-run, vehicle deviation from expected performance can be
automatically identified in the post-run data. Such capability is only now
becoming available due to the rapid increase in understanding of ANN framework
and available computing power in the past decade. The ANN trained for the
purpose of this investigation is relatively simple, to keep the computing
requirements within the parameters of a modern desktop PC. This ANN showed
potential in predicting vehicle performance, particularly during transient
events within the run data. However, there were also several performance cases,
such as steady state operation and cases which did not have sufficient training
data, where the ANN showed deficiencies. It is expected that as computational
power becomes more readily available, ANN understanding matures, and more
training data is acquired from real world tests, the performance predictions of
the ANN will surpass traditional algorithms and manual human review.
