Lifetime Analysis of Automotive Batteries usingRandom Forests and Cox Regression

Abstract: Worn out batteries is a frequent cause of unplanned immobilization of trucks, causing disrupted operations for haulage contractors. To avoid unplanned maintenance, it is desirable to accurately estimate the battery lifetime to perform preventive replacements before the components fail. This master’s thesis has investigated how technical features and operational conditions influence the lifetime of truck batteries and how the risk of failure can be modeled. A support vector machine classifier has been used to examine how well the available data discriminate the vehicles with battery failure from those without. The performance of the classifier, according to the area under the receiver operating characteristic curve, was 70.54% and 76.95% for haulage and distribution vehicles respectively. Maximum likelihood estimation was applied to censored failure time data showing that, if failures that occurred within 100 days after delivery were omitted, both failure data sets were normal distribution on a 95% significance level. To investigate how different features influence the lifetime, random forests and Cox regression were applied on two different models, one intended to be applied for new vehicles and one for vehicles that have been operating for a time, hence having an age covariate. The results from the first model were satisfying, having significant Cox coefficients and low Brier scores for both random forests and Cox. The second model however did not give credible results, having non-significant regression coefficients.