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

Rocco Tarantino; Kathiusca Cabezas; Francklin Rivas-Echeverria; Eliezer Colina-Morles

doi:10.1117/12.421159

21 March 2001 Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks

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

Proceedings Volume 4390, Applications and Science of Computational Intelligence IV; (2001) https://doi.org/10.1117/12.421159
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

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 to 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 presence of non-decoupled failures.

Citation Download Citation

Rocco Tarantino, Kathiusca Cabezas, Francklin Rivas-Echeverria, and Eliezer Colina-Morles "Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks", Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); https://doi.org/10.1117/12.421159

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available