Heavy-duty truck battery failure prognostics using random survival forests
Predicting lead-acid battery failure is important for heavy-duty
trucks to avoid unplanned stops by the road. There are large amount of
data from trucks in operation, however, data is not closely related to
battery health which makes battery prognostic challenging. A new
method for identifying important variables for battery failure
prognosis using random survival forests is proposed. Important
variables are identified and the results of the proposed method are
compared to existing variable selection methods. This approach is
applied to generate a prognosis model for lead-acid battery failure in
trucks and the results are analyzed.
Sergii Voronov, Daniel Jung and Erik Frisk
2016
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