Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Research on the Fault Diagnosis Method for Hoisting Machinery Based on Multi-source Information Fusion and BPNN

Authors
Yi Xie, Jiangwen Zhang
Corresponding Author
Yi Xie
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.70How to use a DOI?
Keywords
multi-source information fusion; back propagation neural network; fault diagnosis; hoisting machinery introduction
Abstract
In this paper, a fault diagnosis method for hoisting machinery based on multi-source information fusion and BPNN that has a fast training time and a high accuracy rate and can be converted to on-line monitoring system easily is provided. This method can be used to help people to real-time monitoring equipment and components and trace hazards. Compared with traditional methods currently used, the method has higher diagnostic accuracy and wider diagnostic range.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.70How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yi Xie
AU  - Jiangwen Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Research on the Fault Diagnosis Method for Hoisting Machinery Based on Multi-source Information Fusion and BPNN
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 282
EP  - 285
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.70
DO  - https://doi.org/10.2991/icaita-16.2016.70
ID  - Xie2016/01
ER  -