Random Forests for Industrial Device Functioning Diagnostics Using
  Wireless Sensor Networks
  In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to spread out. Using a wireless sensor network can solve this
problem, but this latter is more subjected to flaws. Furthermore, the networks'
topology often changes, leading to a variability in quality of coverage in the
targeted area. Diagnostics at the sink level must take into consideration that
both the number and the quality of the provided features are not constant, and
that some politics like scheduling or data aggregation may be developed across
the network. The aim of this article is ($1$) to show that random forests are
relevant in this context, due to their flexibility and robustness, and ($2$) to
provide first examples of use of this method for diagnostics based on data
provided by a wireless sensor network.
