Data based fault isolation in complex measurement systems using models on demand

Hajrudin Efendic, Andreas Schrempf, Luigi Del Re

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Fault detection in complex plants has to cope with substantial problems due to the very large data amount. In many cases, adequate plant descriptions are not available, so that models has to be built up on line. To achieve this in a sensible time, data have to be sorted and this almost always leads to an information compression. While this proves very helpful to detect faults, it represents a serious obstacle for the identification of the faulty channel, as the existing partial models do not usually span a full measurement space or do it with a very poor condition. This paper proposes to use a double technique to achieve this end, first improving the fault isolation process through a gradient based method, but then recurring to model-on-demand methods which can be used to complete the required measurement space to yield the precise fault channel information.

Original languageEnglish
Pages (from-to)1047-1052
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume36
Issue number5
DOIs
Publication statusPublished - 2003
Event5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Safeprocess 2003 - Washington, United States
Duration: 9 Jun 199711 Jun 1997

Keywords

  • Analytical Redundancy
  • Complex Measurement Systems
  • Fault Detection (FD)
  • Fault Isolation (FI)
  • Model-on-Demand (MoD)

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