A Forecasting Model to Equipment Health Status Based on PSR&Elman Technology
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.
health status phase space reconstruction dynamic neural network
Yuefeng Chen Yuansheng Dong Huting Song Feng Liu
63963 units and 66393 units The fourth department Beijing, China Department of Technidal Support Engineering Academy of Armored force Engineering Beijing, China 63963 units The fourth department Beijing, China
国际会议
贵阳
英文
304-307
2011-06-12（万方平台首次上网日期，不代表论文的发表时间）