Title :
Fault diagnosis of air-conditioning fan based on RBF neural networks algorithm
Author_Institution :
Dept. of Building Environ. & Equip. Eng., Donghua Univ., Shanghai, China
Abstract :
In order to overcome the problems of slow rate of convergence, falling easily into local minimum and instability of learning performance caused by initial value in BP algorithm, the diagnosis method based on RBF neural networks was proposed. And the diagnosis method is applied to air-conditioning fan fault diagnosis. The result shows that RBF network has very high learning convergence speed and better classifying performance. RBF network has good practicality in the field of equipment fault diagnosis.
Keywords :
air conditioning; backpropagation; fans; fault diagnosis; learning (artificial intelligence); mechanical engineering computing; quality assurance; radial basis function networks; RBF neural networks algorithm; air conditioning fan fault diagnosis; backpropagation algorithm; diagnosis method; equipment fault diagnosis; high learning convergence speed; Classification algorithms; Convergence; Cryptography; Air-conditioning fan; Fault Diagnosis; RBF neural networks;
Conference_Titel :
Circuits,Communications and System (PACCS), 2010 Second Pacific-Asia Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-7969-6
DOI :
10.1109/PACCS.2010.5626978