Application LAMDA algorithm for Fault Detection and Isolation

Author

Krivanek, Vaclav

Author_Institution

Univ. of Defence, Brno, Czech Republic

fYear

2011

fDate

1-3 June 2011

Firstpage

46

Lastpage

51

Abstract

For many complex technical devices the classic feed-back control does not satisfy. Therefore the Fault Tolerant Control (FTC) with progressive the Fault Detection & Isolation (FDI) is applied. The objective of this article is to describe the problem with application of data-based approach into the FDI block. The data-based methods (in our case we talk mainly about classification-based methods) provide the final information purely from the process data. The Learning Algorithm for Multivariate Data Analysis (LAMDA) is tested here for FDI task. The results of the recherche are demonstrated in a two-tank system utilized as a benchmark.

Keywords

data analysis; fault diagnosis; fault tolerance; learning (artificial intelligence); FDI block; LAMDA algorithm; classification based method; data based approach; fault detection; fault isolation; fault tolerant control; learning algorithm for multivariate data analysis; two-tank system; Benchmark testing; Context; Control systems; Fault tolerance; Fault tolerant systems; Mathematical model; Valves; Data-based diagnostic; FDI; LAMDA; SALSA; classification; fault tolerant control;

fLanguage

English

Publisher

ieee

Conference_Titel

MECHATRONIKA, 2011 14th International Symposium

Conference_Location

Trencianske Teplice

Print_ISBN

978-1-61284-821-1

Type

conf

DOI

10.1109/MECHATRON.2011.5961069

Filename

5961069