Predictive Maintenance for Transport Systems – Employing Model Ensembles for Online State Detection

Jan Zenisek, Michael Affenzeller, Christoph Sievi, Mathias Silmbroth, Josef Wolfartsberger

Research output: Contribution to conferenceAbstract

Abstract

Predictive Maintenance (PdM) plays an important role in detecting potential problems and preventing unexpected equipment failures in the industrial area. Transport systems represent another application domain where PdM could lead to higher availability and lower maintenance costs. In this paper, we propose a machine learning approach to predict the Remaining Useful Life (RUL) of turbofan units in aircraft.
Original languageEnglish
Number of pages2
Publication statusPublished - 2017
EventIAUP Triennial Conference - Wien, Austria
Duration: 5 Jul 20178 Jul 2017

Conference

ConferenceIAUP Triennial Conference
CountryAustria
CityWien
Period05.07.201708.07.2017

Keywords

  • Predictive Maintenance
  • Datastream Analysis
  • Sliding Window
  • Symbolic Regression
  • Ensemble Modeling

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