TY - GEN
T1 - Sliding window symbolic regression for predictive maintenance using model ensembles
AU - Zenisek, Jan
AU - Affenzeller, Michael
AU - Wolfartsberger, Josef
AU - Silmbroth, Mathias
AU - Sievi, Christoph
AU - Huskic, Aziz
AU - Jodlbauer, Herbert
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85041802127&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74718-7_58
DO - 10.1007/978-3-319-74718-7_58
M3 - Conference contribution
SN - 9783319747170
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 481
EP - 488
BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
A2 - Moreno-Diaz, Roberto
A2 - Quesada-Arencibia, Alexis
A2 - Pichler, Franz
PB - Springer
T2 - 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Y2 - 19 February 2017 through 24 February 2017
ER -