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Generation of a diagnosis model for hybrid-electric vehicles using machine learning.
- Source :
-
Microprocessors & Microsystems . Jun2020, Vol. 75, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- Online fault-diagnosis on system level for complex mechatronic systems takes multiple sensor measurements of the various components into account and contributes to a significantly increased system reliability by tracking down faults in the system at run time, enabling fault-specific recovery actions, such as reconfigurations. Ongoing efforts in the technological development of automobiles, especially in the field of driver assistance systems, yield more and more safety-critical systems, e.g., breaking control systems, and thus generate a high demand for reliable online diagnosis systems. In order to perform fault-diagnosis on system level, the interrelations between all measurements must be determined, which is a challenging and often demanding task done by human system experts. In this paper we present a systematic approach based on machine learning to establish online diagnosis for a hybrid-electric vehicle model in the context of the DAKODIS research project. With this paper we publish the Matlab/Simulink HEV research platform including a fault injection framework and data processing algorithms for active fault-diagnosis and recovery evaluations. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01419331
- Volume :
- 75
- Database :
- Academic Search Index
- Journal :
- Microprocessors & Microsystems
- Publication Type :
- Academic Journal
- Accession number :
- 143364312
- Full Text :
- https://doi.org/10.1016/j.micpro.2020.103071