On the relevance of graphical causal models for failure detection for industrial machinery

A. Kosorus, B. Freudenthaler, J. Küng. On the relevance of graphical causal models for failure detection for industrial machinery. volume 8111, pages 174-181, 12, 2013.

Autoren
  • Andreaa-Hilda Kosorus
  • Bernhard Freudenthaler
  • Josef Küng
BuchComputer Aided Systems Theory - EUROCAST 2013, Revised Selected Papers, Part I
TypIn Buch
VerlagSpringer
SerieLecture Notes in Computer Science
Band8111
ISBN978-3-642-53855-1
Monat12
Jahr2013
Seiten174-181
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 L1
regularization) perform better than traditional full models.