Title :
Fault diagnosis based on radial basis function neural network in Particleboard Glue Mixing & Dosing System
Author :
Liu, Yaqin ; Zhang, Xiaopeng ; Hua, Jun
Author_Institution :
Northeast Forestry Univ., Harbin
Abstract :
In this paper, a new style radial basis function neural network (RBF NN) is used for fault diagnosis in Particleboard Glue Mixing & Dosing System, which is firstly used in this field. The structure and its training algorithm of the network are discussed and the training algorithm chosen in the article is a self-adapt clustering training algorithm. The results of the simulation and fault tolerance test confirm that the proposed method can diagnose the fault of the system quickly and correctly. Furthermore, it has the ability of forecast warning.
Keywords :
adhesives; fault diagnosis; fault tolerance; learning (artificial intelligence); pattern clustering; radial basis function networks; wood processing; RBFNN; dosing system; fault diagnosis; fault tolerance; forecast warning; particleboard glue mixing system; radial basis function neural network; self-adaptive clustering training algorithm; simulation; Clustering algorithms; Fault diagnosis; Feedforward systems; Iterative algorithms; Kernel; MATLAB; Neural networks; Neurons; Pattern clustering; Radial basis function networks; Fault Diagnosis; Particleboard Glue Mixing & Dosing System; Radial Basis Function Neural Network;
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
DOI :
10.1109/CCDC.2008.4597418
