Skip to Main Content
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.