An intelligent system for integrated predictive diagnosis

Author

Diwakar, S. ; Essawy, M.A. ; Sabatto, S. Zein

Author_Institution

Dept. of Electr. & Comput. Eng., Tennessee State Univ., Nashville, TN, USA

fYear

1998

fDate

8-10 Mar 1998

Firstpage

179

Lastpage

183

Abstract

We present an automated system for integrated predicted diagnosis. This fault diagnosis method was tested on vibration data recorded from an aft main power transmission of a US Navy CH-46E helicopter. The fault diagnosis system is based on a neuro-fuzzy algorithm. First frequency domain analysis techniques were used to extract features from the vibration signals. These features were then clustered by a self organizing map neural network and identified by a backpropagation network. The decisions from different channels or sensors were fused using fuzzy logic techniques

Keywords

backpropagation; fault diagnosis; feature extraction; frequency-domain analysis; fuzzy logic; helicopters; knowledge based systems; pattern classification; self-organising feature maps; sensor fusion; US Navy CH-46E helicopter; aft main power transmission; backpropagation network; fault diagnosis method; frequency domain analysis techniques; fuzzy logic techniques; integrated predictive diagnosis; intelligent system; neuro-fuzzy algorithm; self organizing map neural network; vibration data; vibration signal; Backpropagation algorithms; Clustering algorithms; Data mining; Fault diagnosis; Feature extraction; Frequency domain analysis; Helicopters; Intelligent systems; Power transmission; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on

Conference_Location

Morgantown, WV

ISSN

0094-2898

Print_ISBN

0-7803-4547-9

Type

conf

DOI

10.1109/SSST.1998.660042

Filename

660042