Title :
Research on Fan Machinery Intelligence Fault Diagnosis System of Datum-Fusional Neural Network
Author :
Yanmin, Lu ; Kangling, Fang ; Yonglong, Zeng
Author_Institution :
Coll. of Inf. Sci. & Eng., Wuhan Univ. of Sci. & Technol., Wuhan, China
Abstract :
This paper uses information fusion and neural net technology as theoretical basis for building a fused neural network model and a neural network fault diagnosis for a fan. In the model, the homogeneous information is fused by the most closest clustering algorithm and the multi-source information is composed by the artificial neural network technology. And then the fused results are used as an input source for neural network diagnosis. In such a way, the intelligent diagnosis is realized and the effect is good.
Keywords :
fans; fault diagnosis; machinery; mechanical engineering computing; neural nets; pattern clustering; sensor fusion; Datum-Fusional neural network; clustering algorithm; fan machinery intelligence fault diagnosis system; fused neural network model; information fusion; intelligent diagnosis; multisource information; neural network fault diagnosis; Artificial intelligence; Artificial neural networks; Fault diagnosis; Intelligent networks; Intelligent sensors; Intelligent structures; Intelligent systems; Machine intelligence; Machinery; Neural networks; ANN; Fault diagnosis; Information fusion; fan machinery;
Conference_Titel :
Computing, Control and Industrial Engineering (CCIE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-4026-9
DOI :
10.1109/CCIE.2010.66