Frequency Domain Identification and Identifiability Analysis of a Nonlinear Vehicle Drivetrain Model Article Swipe
Eduard Popp
,
Mathias Tantau
,
Mark Wielitzka
,
Tobias Ortmaier
,
Dennis Giebert
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.23919/ecc.2019.8795688
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.23919/ecc.2019.8795688
Physical parameters of a vehicle drivetrain are required in many applications. In the context of fault diagnosis, for example, knowledge about the installed components or parts can provide insights in order to verify if they behave in accordance with standards or deviate from them in a way that adversely affects the operating performance. For this purpose a frequency domain identification approach is presented, which is based only on standard mounted sensors. In the presented method in particular nonlinear effects are taken into account resulting from backlash. In order to guarantee a unique parameter set a local identifiability analysis is performed. The main idea of the method is to exploit the dependency between the frequency response of the nonlinear system and the magnitude of the test-signal to improve optimization of the physical parameters. Finally, identification results using real measurement data are presented.
Related Topics
Concepts
Identifiability
Drivetrain
Backlash
Computer science
Context (archaeology)
Frequency domain
Nonlinear system
Control theory (sociology)
Identification (biology)
Dependency (UML)
Nonlinear system identification
Frequency response
Control engineering
System identification
Engineering
Data modeling
Artificial intelligence
Machine learning
Torque
Electrical engineering
Physics
Computer vision
Database
Botany
Quantum mechanics
Biology
Paleontology
Thermodynamics
Control (management)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.23919/ecc.2019.8795688
- OA Status
- green
- Cited By
- 7
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2967011319
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2967011319Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.23919/ecc.2019.8795688Digital Object Identifier
- Title
-
Frequency Domain Identification and Identifiability Analysis of a Nonlinear Vehicle Drivetrain ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-01Full publication date if available
- Authors
-
Eduard Popp, Mathias Tantau, Mark Wielitzka, Tobias Ortmaier, Dennis GiebertList of authors in order
- Landing page
-
https://doi.org/10.23919/ecc.2019.8795688Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.repo.uni-hannover.de/handle/123456789/10895Direct OA link when available
- Concepts
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Identifiability, Drivetrain, Backlash, Computer science, Context (archaeology), Frequency domain, Nonlinear system, Control theory (sociology), Identification (biology), Dependency (UML), Nonlinear system identification, Frequency response, Control engineering, System identification, Engineering, Data modeling, Artificial intelligence, Machine learning, Torque, Electrical engineering, Physics, Computer vision, Database, Botany, Quantum mechanics, Biology, Paleontology, Thermodynamics, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1, 2022: 1, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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