Abstract
Predictive Maintenance (PdM) is among the trending topics
in the current Industry 4.0 movement and hence, intensively investigated.
It aims at sophisticated scheduling of maintenance, mostly in the area
of industrial production plants. The idea behind PdM is that, instead
of following fixed intervals, service actions could be planned based upon
the monitored system condition in order to prevent outages, which leads
to less redundant maintenance procedures and less necessary overhauls.
In this work we will present a method to analyze a continuous stream of
data, which describes a system's condition progressively. Therefore, we
motivate the employment of symbolic regression ensemble models and
introduce a sliding-window based algorithm for their evaluation and the
detection of stable and changing system states.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Number of pages | 8 |
| Publication status | Published - 2017 |
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