DocumentCode :
1748871
Title :
A neurofuzzy network and its application to machine health monitoring
Author :
Meesad, Phayung ; Yen, Gary G.
Author_Institution :
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
Volume :
3
fYear :
2001
fDate :
2001
Firstpage :
2298
Abstract :
An innovative neurofuzzy network is proposed for pattern classification applications to machine health monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, online, and incremental learning algorithm. To evaluate the proposed network, the numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a U.S. Navy CH-46E helicopter test stand. The proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification
Keywords :
condition monitoring; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; U.S. Navy CH-46E helicopter test stand; Westland data set; fuzzy if-then rules; fuzzy set interpretation; hybrid supervised learning scheme; imprecise information; machine health monitoring; neural network architecture; neurofuzzy network; pattern classification; vibration data; Condition monitoring; Fuzzy neural networks; Fuzzy sets; Helicopters; Neural networks; Numerical simulation; Pattern classification; Performance evaluation; Supervised learning; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.938525
Filename :
938525
Link To Document :
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