Sliding window symbolic regression for predictive maintenance using model ensembles

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
PublisherSpringer Verlag
Pages481-488
Number of pages8
ISBN (Print)9783319747170
DOIs
Publication statusPublished - 2018
Event16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain
Duration: 19 Feb 201724 Feb 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10671 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
CountrySpain
CityLas Palmas de Gran Canaria
Period19.02.201724.02.2017

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