Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks

Rocco Tarantino, Kathiusca Cabezas, Francklin Rivas-Echeverría, Eliezer Colina-Morles

Research output: Contribution to journalArticlepeer-review


The present paper proposes a new layout for failure detection and diagnosis in industrial dynamic systems in which, failure vector decoupling is not always possible, due the failure intrinsic propagation. In this case diagnosis can be determined due to the existing correlation between the failure vector and residual vector time patterns. The greatest benefit of this study is the failure detection method (Luenberger observer based detection filter) through vectorial residual generation combined with the pattern recognition technique based on neural networks theory. The synergy of both methods offer a wider application range to diagnosis problem solutions, in systems under the presence of non-decoupled failures.

Original languageEnglish (US)
Pages (from-to)105-116
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2001
Externally publishedYes


  • Artificial intelligence
  • Detection
  • Detection filter
  • Failure diagnosis
  • Luenberger observer
  • Neural networks

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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