Home > Workload Solutions > Data Analytics > Guides > Ready Solutions for AI & Data Analytics: Edge Analytics for Industry 4.0 with Confluent Platform > Overview
Intelligent machine scenarios focus on performance improvement, improving output quality, and arguably the most lucrative area - preventing unplanned outages through data model driven predictive maintenance. Since the inception of Industry 4.0, the goal of estimating remaining-useful-life (RUL) remains elusive. (Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008, October). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1-9). IEEE.) (Bengtsson, M., & Lundström, G. (2018). On the Importance of Combining “the New” with “the Old”—One Important Prerequisite for Maintenance in Industry 4.0. Procedia Manufacturing, 25, 118-125.)
Data model driven predictive maintenance requires the integration of Industrial IoT (IoT) data management technologies with data science capabilities that are the vision of Industry 4.0. This use case was constructed to exercise all the important components that are required to: