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On the Relevance of Graphical Causal Models for Failure Detection for Industrial Machinery

  • A. H. Kosorus
  • M. Zhariy
  • T. Natschläger
  • B. Freudenthaler
  • Josef Küng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

Abstract

Assessing the reliability of industrial machinery is an important aspect within maintenance processes in order to maximize productivity and efficiency. In this paper we propose to use graphical models for fault detection in industrial machinery within a condition-based maintenance setting. The contribution of this work is based on the hypothesis that during fault free operation the causal relationships between the observed measurement channels are not changing. Therefore, major changes in a graphical model might imply faulty changes within the machine’s functionality or its properties. We compare and evaluate four methods for the identification of potential causal relationships on a real world inspired use case. The results indicate that sparse models (using L 1 regularization) perform better than traditional full models.

Keywords

fault detection and diagnosis graphical models causality condition-based maintenance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • A. H. Kosorus
    • 1
  • M. Zhariy
    • 2
  • T. Natschläger
    • 2
  • B. Freudenthaler
    • 2
  • Josef Küng
    • 1
  1. 1.Institute of Application Oriented Knowledge ProcessingJohannes-Kepler UniversityLinzAustria
  2. 2.Software Competence Center HagenbergAustria

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