Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Bergmeir, Philipp; Nitsche, Christof; Nonnast, Jürgen; Bargende, Michael
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2289
• Page End: 2304
• Publication Date: 2016-11
• Subjects: Hybrid electric vehicle -- Balanced random forests -- Univariate and multivariate decision trees -- Orthogonal and oblique splits -- Load spectrum data; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2065-y
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.