# Random Forests for Industrial Device Functioning Diagnostics Using Wireless Sensor Networks

## TL;DR

This paper demonstrates that random forests are effective and robust for diagnosing industrial device functioning using data from wireless sensor networks with variable features and network conditions.

## Contribution

It introduces the application of random forests to wireless sensor network-based diagnostics, addressing challenges of variable features and network topology changes.

## Key findings

- Random forests perform well with variable feature sets.
- Wireless sensor networks can effectively support device diagnostics.
- The method is robust to network topology changes.

## Abstract

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.