Feature extraction of internal dynamics of an engine air path system: Deep autoencoder approach. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Shimizu, Kazuhiro; Nakada, Hayato; Kashima, Kenji
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 736
• Page End: 741
• Publication Date: 2018
• Subjects: Engine air path system -- Machine learning -- dimensionality reduction; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.167
• Languages: English
• ISSNs: 2405-8963

Abstract

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.