Incremental technique is an efficient mechanism for dealing with dynamic knowledge discovery. The fuzzy probabilistic rough set model on dual universes (FPRSMDU) is an integrated generalization of classic rough set theory (RST) on fuzziness, probability and dual universes. Although a significant number of RST based research efforts have been directed toward developing incremental algorithms to speed up computation of approximations, feature selection, as well as rule extraction in the context of dynamical information systems, there remains lack of effort towards incorporating the incremental method into knowledge updating in the framework of FPRSMDU. Approximations of FPRSMDU are fundamental concepts, which can be used for knowledge discovery in big data or other related work, need to be updated effectively when the objects of two universes vary with time. In light of these issues, an incremental approach for updating approximations in FPRSMDU is proposed while multiple objects inserting into or deleting from the two universes. The validity of the proposed method has been exemplified by employing an illustrative example.