Title :
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
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;
Conference_Titel :
System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on
Conference_Location :
Morgantown, WV
Print_ISBN :
0-7803-4547-9
DOI :
10.1109/SSST.1998.660042
