Title :
A forecasting model to equipment health status based on PSR&Elman technology
Author :
Chen, Yuefeng ; Song, Huting ; Dong, Yuansheng ; Liu, Feng
Author_Institution :
63963 Unit, Fourth Dept., PLA, Beijing, China
Abstract :
It plays a crucial role in autonomic logistics or maintenance decision-making on condition to forecast equipment health status. However it was influenced by many various factors with complexity as variable, strong coupling, nonlinear and dynamic. The difficulty to forecast equipment health status lies in treating time sequence characteristic of health status index and complexity characteristic of equipment system which need a dynamic technology to map its inner status. Fresh technology of phase space reconstruction and Elman neural network were introduced. Equipment health status index was reconstructed in the phase space technology and the forecasting model was built up with dynamic neural network. The application case on this model was carried out with forecasting equipment accelerating time. The result shows an effective approach was explored to this problem.
Keywords :
condition monitoring; decision making; forecasting theory; logistics; maintenance engineering; neural nets; production equipment; Elman neural network; PSR technology; autonomic logistics; complexity characteristic; dynamic neural network; equipment health status index; forecasting equipment accelerating time; maintenance decision making; phase space reconstruction; time sequence characteristic; Acceleration; Delay; Forecasting; Indexes; Maintenance engineering; Predictive models; Training; dynamic neural network; health status; phase space reconstruction;
Conference_Titel :
Reliability, Maintainability and Safety (ICRMS), 2011 9th International Conference on
Conference_Location :
Guiyang
Print_ISBN :
978-1-61284-667-5
DOI :
10.1109/ICRMS.2011.5979280
