Machine Learning on Terrain Data and Logged Vehicle Data to Gain Insights into Operating Conditions for an Articulated Hauler : Machine Learning on Terrain Data and Logged Vehicle Data to Gain Insights into Operating Conditions for an Articulated Hauler

Abstract: Manufacturers can develop next-generation production and service for their customers by the data gathered and analyzed from customers’ usage conditions. In this research, the operating condition of articular haulers is collected and analyzed through machine learning algorithms to predict the type of operational topographies and road surface. To achieve that, elevation data and satellite images, which were gathered from Microsoft Azure Maps, are used as data sources to identify the topography and road surface on which machines operated. In the end, two machine learning models are trained with machines’ inclination records and road roughness records, respectively, to classify the topography and road surface. For the topography classifier, the topography is categorized into four terrain labels, including "Low Hills", "Mountains", "Plains", and "Tablelands & High Hills". The road surface is classified into "Paved" and "Unpaved". A Convolutional Neural Network (CNN) image classification model is built for labeling satellite images instead of labeling manually. The results indicate that the prediction for topography labels "Plains" and "Tablelands & High Hills" has superior performance, which accounts for the majority of the raw dataset; on the contrary, the road surface classifier still needs further improvement in the future. In addition, an analysis and discussion regarding the imbalanced dataset are included, and it shows the limited effect on an extremely imbalanced dataset. Finally, the conclusion and future work are given.