Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{6351,
-
author = "Jan Zenisek and Michael Affenzeller and
Josef Wolfartsberger and Mathias Silmbroth and
Christoph Sievi and Aziz Huskic and Herbert Jodlbauer",
-
title = "Sliding Window Symbolic Regression for Predictive
Maintenance using Model Ensembles",
-
booktitle = "Computer Aided Systems Theory, EUROCAST 2017",
-
year = "2017",
-
editor = "Roberto Moreno-Diaz and Franz Pichler and
Alexis Quesada-Arencibia",
-
volume = "10671",
-
series = "Lecture Notes in Computer Science",
-
pages = "481--488",
-
address = "Las Palmas de Gran Canaria, Spain",
-
month = feb,
-
publisher = "Springer",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-3-319-74718-7",
-
URL = "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_58",
-
DOI = "doi:10.1007/978-3-319-74718-7_58",
-
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 analyse 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.",
-
notes = "Published 2018?",
- }
Genetic Programming entries for
Jan Zenisek
Michael Affenzeller
Josef Wolfartsberger
Mathias Silmbroth
Christoph Sievi
Aziz Huskic
Herbert Jodlbauer
Citations