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Finding Driving Style Patterns in Caterpillar Machine Data


PyData Chicago 2016


Identifying predominant driving-style patterns in logged time series data of Caterpillar machines is daunting due to the nature and size of the data. However, insight gained from field data can deliver optimized powertrain control software and better machine performance. A solution for finding patterns was built using engineered features, dimensionality reduction, and unsupervised learning.

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