Variable selection for heavy-duty vehicle battery failure prognostics using random survival forests
Prognostics and health management is a useful tool for more flexible
maintenance planning and increased system reliability. The
application in this study is lead-acid battery failure prognosis for
heavy-duty trucks which is important to avoid unplanned stops by the
road. There are large amounts of data available, logged from trucks
in operation. However, data is not closely related to battery health
which makes battery prognostic challenging. When developing a
data-driven prognostics model and the number of available variables
is large, variable selection is an important task, since including
non-informative variables in the model have a negative impact on
prognosis performance. Two features of the dataset has been
identified, 1) few informative variables, and 2) highly correlated
variables in the dataset. The main contribution is a novel method
for identifying important variables, taking these two properties
into account, using Random Survival Forests to estimate prognostics
models. The result of the proposed method is compared to existing
variable selection methods, and applied to a real-world automotive
dataset. Prognostic models with all and reduced set of variables are
generated and differences between the model predictions are
discussed, and favorable properties of the proposed approach are
highlighted.
Sergii Voronov, Daniel Jung and Erik Frisk
2016
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