Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles

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@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