REVIEW ARTICLE
Study of the Fault Diagnosis Model of High Pressure Roller Mill Gearbox Lubrication System
Xijuan Wang*, 1, Lei Zhang1, Jingxiao Feng2, Wei Zhou2, Haiyan Gao2
1 School of Physical and Electrical Information, Luoyang Normal University, Luoyang 471022, Henan, China
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 865
Last Page: 869
Publisher Id: TOMEJ-9-865
DOI: 10.2174/1874155X01509010865
Article History:
Received Date: 17/02/2014Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 7/10/2015
Collection year: 2015
© 2015 Wang 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.
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.
Abstract
The fault phenomenon of high pressure roller mill gearbox lubrication system is not easy to be find in many cases, the fault of system is easy to be ignored, and it is more difficult to judge with the traditional method. For this reason, the fault diagnosis model of the particle swarm neural network has been established by using actual sample data, determining the cause of fault through the actual monitoring data. The practice has proved that it has better prediction effect.
Keywords: BP network, fault diagnosis, high pressure roller mill, particle swarm algorithm.
