Engine Health Monitoring for engine fleets using fuzzy radviz

Author

Martinez, A. ; Sanchez, L. ; Couso, Ines

Author_Institution

Rolls-Royce Deutschland Ltd. & Co. KG, Blankenfelde-Mahlow, Germany

fYear

2013

fDate

7-10 July 2013

Firstpage

1

Lastpage

8

Abstract

A new algorithm for assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. The diagnostic tool quantifies step changes, shifts and trends in EHM data by means of a transformation that aggregates concurrent readings of EHM data into a single fuzzy state. A Genetic Fuzzy System is used to detect the occurance of a specific trend of interest in the sequence of states. The activation of the rules is represented in a 2D map by means of an extension of the Radviz visualization algorithm to fuzzy data.

Keywords

aerospace engineering; aerospace engines; aircraft; condition monitoring; data visualisation; fault diagnosis; fuzzy set theory; genetic algorithms; mechanical engineering computing; 2D map; EHM data; Radviz visualization algorithm; aircraft; diagnostic tool; engine fleets; engine health monitoring; fuzzy Radviz; fuzzy data; genetic fuzzy system; rule activation; single fuzzy state; states sequence; Bandwidth; Engines; Maintenance engineering; Market research; Monitoring; Temperature measurement; Turbines; Engine Health Monitoring; Fuzzy Radviz; Genetic Fuzzy Systems; Low Quality Data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2013 IEEE International Conference on

Conference_Location

Hyderabad

ISSN

1098-7584

Print_ISBN

978-1-4799-0020-6

Type

conf

DOI

10.1109/FUZZ-IEEE.2013.6622420

Filename

6622420