Journal of System Simulation
Abstract
Abstract: Aiming at the difficulty in discovering and eliminating the system faults of automobile coating equipments in time, a new method of fault diagnosis based on extension neural network was proposed. The feature of extension theory was used in managing the structured information through qualitative and quantitative description, and it was also combined by the characteristic of parallel construct in neural network. So the extension reasoning process was completed by means of the parallel distributed processing construct of the network. Matter-element input and output models were established according to the equipment monitoring parameters and fault types for the heating system. And parameter samples were taken into training, and a comparative simulation experiment was made for the result. The experiment reveals that the extension neural network has a simpler construct and can respond faster compared with the traditional neural network.
Recommended Citation
Ye, Yongwei; Ren, Shedong; Ye, Lianqiang; Ge, Shenhao; and Qian, Zhiqin
(2020)
"Fault Diagnosis for Automobile Coating Equipments Based on Extension Neural Network,"
Journal of System Simulation: Vol. 27:
Iss.
3, Article 14.
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol27/iss3/14
First Page
542
Revised Date
2014-11-25
DOI Link
https://doi.org/
Last Page
548
CLC
TP391
Recommended Citation
Ye Yongwei, Ren Shedong, Ye Lianqiang, Ge Shenhao, Qian Zhiqin. Fault Diagnosis for Automobile Coating Equipments Based on Extension Neural Network[J]. Journal of System Simulation, 2015, 27(3): 542-548.
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