Fault Diagnosis for Automobile Coating Equipments Based on Extension Neural Network

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.

Key words: fault diagnosis, neural networks, extenics, heating system

CLC Number:

TP391

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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