Abstract
Predictive maintenance poses a new way to minimize
costs and downtime of machinery. The combination of
sensor data, intelligent algorithms and computing power
allows this new approach to monitor the current healthstate
of machinery and detect possible failures early on
or even in advance. Previous work in this field regarding
radial fans focused on aspects such as vibration and
noise, whereas this paper concentrates on the influence
of multiple sensor data when modeling radial fans. In a
case study multiple sensors are mounted on a radial fan
and the importance of their signals on damage prediction
is presented. The correlation between them is analyzed
and the variable impact of sensor signals for
approximating the rotational speed of a healthy and a
damaged radial fan is identified.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 30th European Modeling and Simulation Symposium EMSS2018 |
| Number of pages | 9 |
| Publication status | Published - 2018 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102011 Formal languages
- 102022 Software development
- 102031 Theoretical computer science
- 603109 Logic
- 202006 Computer hardware
JKU Focus areas
- Computation in Informatics and Mathematics