Equipment Failure Prediction based on the Improved Gray Prediction

DOI

10.2991/icamia-15.2015.30How to use a DOI?

Keywords

equipment failure; gray prediction; background values

Abstract

In order to reduce the equipment failure frequency and maintenance cost, equipment failure must be effectively predicted. Consider that the actual failure interval of equipments is to contribute to maintenance planning and scheduling, it is used to statistical analysis the failure trend. According to the historical data of maintenance, the failure time distribution function can be built by gray prediction theory. This paper discussed the influence on model relative error made by the optimization of GM(1, 1) model background value. The result is that the model relative error can be reduced through adjust the value of the background. The application of PSO optimized the background value of GM(1,1) model. Finally, an engineering example verified the conclusion.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - She Liu
AU  - Shijie Wang
AU  - Huizhi Ren
PY  - 2015/12
DA  - 2015/12
TI  - Equipment Failure Prediction based on the Improved Gray Prediction
BT  - Proceedings of the 2015 International Conference on Advanced Manufacturing and Industrial Application
PB  - Atlantis Press
SP  - 120
EP  - 122
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamia-15.2015.30
DO  - 10.2991/icamia-15.2015.30
ID  - Liu2015/12
ER  -