Generation of a diagnosis model for hybrid-electric vehicles using machine learning.

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]