A Forest-based Algorithm for Selecting Informative Variables Using Variable Depth Distribution
Predictive maintenance of systems and their components in technical
systems is a promising approach to optimize system usage and reduce
system downtime. Various sensor data are logged during system
operation for different purposes, but sometimes not directly related
to the degradation of a specific component. Variable selection
algorithms are necessary to reduce model complexity and improve
interpretability of diagnostic and prognostic algorithms. This paper
presents a forest-based variable selection algorithm that analyzes the
distribution of a variable in the decision tree structure, called
Variable Depth Distribution, to measure its importance. The proposed
variable selection algorithm is developed for datasets with correlated
variables that pose problems for existing forest-based variable
selection methods. The proposed variable selection method is evaluated
and analyzed using three case studies: survival analysis of lead-acid
batteries in heavy-duty vehicles, engine misfire detection, and a
simulated prognostics dataset. The results show the usefulness of the
proposed algorithm, with respect to existing forest-based methods, and
its ability to identify important variables in different
applications. As an example, the battery prognostics case study shows
that similar predictive performance is achieved when only 17% percent
of the variables are used compared to all measured signals.
Sergii Voronov, Daniel Jung and Erik Frisk
Engineering Applications of Artificial Intelligence,
2021
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