Component failures in hybrid electric vehicles (HEV) can cause high warranty costs for car manufacturers. Hence, in order to (1) predict whether a component of the hybrid power-train of a HEV is faulty, and (2) to identify loads related to component failures, we train several random forest variants on so-called load spectrum data, i.e., the state-of-the-art data employed for calculating the fatigue life of components in fatigue analysis. We propose a parameter tuning framework that enables the studied random forest models, formed by univariate and multivariate decision trees, respectively, to handle the class imbalance problem of our dataset and to select only a small number of relevant variables in order to improve classification performance and to identify failure-related variables. By achieving an average balanced accuracy value of 85.2 %, while reducing the number of variables used from 590 to 22 variables, our results for failures of the hybrid car battery (approx. 200 faulty, 7000 non-faulty vehicles) demonstrate that especially balanced random forests using univariate decision trees achieve promising classification results on load spectrum data. Moreover, the selected variables can be related to component failures of the hybrid power-train.
['Philipp Bergmeir', 'Christof Nitsche', 'Jürgen Nonnast', 'Michael Bargende']
Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data: Balanced random forest approaches employing uni- and multivariate decision trees