Applying Artifical Neural Networks To Predict Nominal Vehicle Performance

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.