Classification of fuel type for predictive maintenance in marine and industrial engines using time series feature extraction based on hypothesis tests and automated machine learning

Predictive maintenance in internal combustion engines can be enhanced by accurately identifying the fuel type based on data collected from sensors or electronic control units (ECUs). This paper presents a study that aims to predict the fuel type (HVO100 or EN590) using machine learning techniques, s...

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Autors principals:	Ning Guo, Erik Jansson, Mattias Johansson, Ronny Lindgren, Andreas Nyman, Jonas Sjöblom
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2026-03-01
Col·lecció:	Applications in Energy and Combustion Science
Matèries:	Internal combustion engine Fuel quality Machine learning Predictive maintenance Time series Tsfresh
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2666352X25001219
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