Resource State Estimation and Intelligent Contingency Forecasting for Spacecraft Mission Control Center

Abstract The paper presents the concept of on-line estimating the state of the Mission Control Center resources and algorithms for forecasting of contingencies. Resource state prediction methods based on the development of a machine learning techniques called association rules are presented. Using this method, the detection of such parameters and their values, the appearance of which, at some point in time, affects the probability of a certain state of the system at another moment in time, and in particular, the occurrence of an emergency. The results of real case studies and specialist interface fragments are presented. System development options are discussed.