Data-driven virtual sensor for powertrains based on transfer learning

Data-driven virtual sensor for powertrains based on transfer learning

Variation in powertrain parameters caused by dimensioning, manufacturing and assembly inaccuracies may prevent model-based virtual sensors from representing physical powertrains accurately. Data-driven virtual sensors employing machine learning models offer a solution for including variations in the...

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Bibliographic Details
Main Authors: Aku Karhinen, Aleksanteri Hamalainen, Mikael Manngard, Jesse Miettinen, Raine Viitala
Format: Article
Language:English
Published: Polish Academy of Sciences 2023-10-01
Series:Bulletin of the Polish Academy of Sciences: Technical Sciences
Subjects:
powertrain
torsional vibration
long short-term memory
virtual sensor
transfer learning
Online Access:https://journals.pan.pl/Content/128877/PDF/BPASTS_2023_71_6_3784.pdf

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https://journals.pan.pl/Content/128877/PDF/BPASTS_2023_71_6_3784.pdf

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