Sensor Fault Detection Using an Extended Kalman Filter and Machine Learning for a Vehicle Dynamics Controller

This paper describes a new sensor fault detection approach for a vehicle dynamics controller. The detection problem is divided into two parts. First, a model-based observer is used to incorporate the knowledge of the system into the fault detection. Next, a data driven classification algorithm based...

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Vydané v:	IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society s. 361 - 366
Hlavní autori:	Ossig, Daniel L., Kurzenberger, Kevin, Speidel, Simon A., Henning, Kay-Uwe, Sawodny, Oliver
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 18.10.2020
Predmet:	automotive applications binary classification problem EKF Fault detection fault diagnosis hybrid fault diagnosis machine learning Machine learning algorithms Mathematical model Observers Roads state estimation Technological innovation Vehicle dynamics wavelet packet transform
ISSN:	2577-1647
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