# Dependability of Sensor Networks for Industrial Prognostics and Health Management

## TL;DR

This paper examines the dependability of interconnected wireless sensor networks used in industrial prognostics and health management, emphasizing the importance of reliable data for accurate maintenance planning.

## Contribution

It is the first to analyze the dependability of wireless sensor networks specifically for prognostics and health management in industrial settings.

## Key findings

- Highlights the critical role of sensor network dependability in prognostics accuracy
- Provides a state-of-the-art review of prognostic and health management activities
- Identifies gaps in research on interconnected sensor networks for maintenance

## Abstract

Maintenance is an important activity in industry. It is performed either to revive a machine/component or to prevent it from breaking down. Different strategies have evolved through time, bringing maintenance to its current state: condition-based and predictive maintenances. This evolution was due to the increasing demand of reliability in industry. The key process of condition-based and predictive maintenances is prognostics and health management, and it is a tool to predict the remaining useful life of engineering assets. Nowadays, plants are required to avoid shutdowns while offering safety and reliability. Nevertheless, planning a maintenance activity requires accurate information about the system/component health state. Such information is usually gathered by means of independent sensor nodes. In this study, we consider the case where the nodes are interconnected and form a wireless sensor network. As far as we know, no research work has considered such a case of study for prognostics. Regarding the importance of data accuracy, a good prognostics requires reliable sources of information. This is why, in this paper, we will first discuss the dependability of wireless sensor networks, and then present a state of the art in prognostic and health management activities.