REVIEW ARTICLE


Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network



Yuan Yuan1, *, Wenjun Meng2, Xiaoxia Sun2
1 1School of Transportation & Logistics, Taiyuan University of Science and Technology, Taiyuan, 030024, China
2 School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China


© 2014 Yuan et al

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Transportation & Logistics, Taiyuan University of Science and Technology, Taiyuan 030024, China; Tel: +8615834029609; E-mail: yuanyuan525622@126.com


Abstract

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Keywords: Belt conveyor, BP neural network, fault diagnosis, fuzzy theory.