The article aims to estimate the uncertainty of possible failure events of redundancy systems based on the cross-entropy (CE) method. Failure events of subsystems and components always result in the incomplete or complete failure of engineering systems, yet optimal condition monitoring of a complex system is heavily dependent on the accuracy analysis of all the failure events of subsystems and components and their interaction effects. The CE method is a versatile tool for estimating probabilities of rare events in complex systems with the least bias beyond conditional constraints. In this paper we introduce the CE method for analyzing the system reliability with the highest uncertainty among all possibilities satisfying supplied moment constraints, and developed numerical CE algorithms capable of estimating the uncertainty of failure modes in an M-dimensional redundancy system domain with moment constraints of order up to N. A general computational framework of event estimation and condition monitoring of redundancy systems is illustrated in which the Monte Carlo simulations and CE optimization algorithms are combined. Numerical results indicate potential improvements in the measure of the uncertainty of redundancy systems that would lead to the best-fit analysis of all the complete or incomplete failure events.