Abstract:
Helicopter load monitoring and other health and usage monitoring system applications regularly involve large datasets and machine learning models. The amount of effort that could be devoted to training an optimal solution could be unlimited and quickly becomes prohibitive. However, there are important analyses and tools that could be implemented upfront to expedite finding well performing models as well as providing insight into the black-box machine learning models. In this work, we explore the use of the Gamma test and feature selection techniques to optimize input parameter sets and reduce dataset size, applying this approach to a helicopter load estimation problem. We demonstrate that we can remove half of the features from the input set and reduce the dataset by over 95% while still maintaining a similar level of accuracy and performance of the load estimation model. By reducing the number of features, we can produce simpler models, which are easier to train and explain.