Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks
Maintenance planning is important in the automo- tive industry as it
will allow fleet owners or regular customers to avoid unexpected
failures of the components. One cause of unplanned stops of heavy-duty
trucks is failure in the lead-acid starter battery. High availability
of the vehicles can be achieved by changing the battery frequently,
but such an approach is expensive both due to the frequent visits to a
workshop and also due to the component cost. Here, a data-driven
method based on Random Survival Forest (RSF) is proposed for
predicting the reliability of the batteries. The data set available
for the study, covering more than 50,000 trucks, has two important
properties. First, it does not contain measurements related directly
to the battery health, secondly there are no time series of
measurements for every vehicle. In this paper, the RSF method is used
to predict the reliability function for a particular vehicle using
data from the fleet of vehicles given that only one set of
measurements per vehicle is available. A theory for confidence bands
for the RSF method is developed that is an extension of an existing
technique for variance estimation in the Random Forest method. Adding
confidence bands to the RSF method gives an opportunity for an
engineer to evaluate the confidence of the model prediction. Some
aspects of the confidence bands are considered: a) their asymptotic
behavior and b) usefulness in model selection. A problem of including
time related variables is addressed in the paper with arguments why it
is a good choice not to add them into the model. Metrics for
performance evaluation are suggested which show that the model can be
used to schedule and optimize the cost of the battery replacement. The
approach is illustrated extensively using the real-life truck data
case study.
Sergii Voronov, Erik Frisk and Mattias Krysander
IEEE Transactions on reliability,
2018
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