Feature extraction of internal dynamics of an engine air path system: Deep autoencoder approach

Abstract

In order to model and understand complex dynamics such as automotive engines, it is meaningful to find a low dimensional structure embedded in a large number of physical variables. In this paper, we utilize several types of autoencoders for feature extraction of internal dynamics data of an engine air path system. In particular, the practical usefulness is examined through its application to dimensionality reduction, state estimation, and data replication. In addition, a unified framework of feature extraction and dynamics identification is also discussed.