Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network
Yuan Yuan1, *, Wenjun Meng2, Xiaoxia Sun2
Identifiers and Pagination:Year: 2014
First Page: 916
Last Page: 921
Publisher Id: TOMEJ-8-916
Article History:Received Date: 08/01/2015
Revision Received Date: 15/01/2015
Acceptance Date: 16/01/2015
Electronic publication date: 31/12/2014
Collection year: 2014
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.
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.