This article continues the author's work on creating digital twins for nuclear power plant (NPP) units using flexible modeling systems integrated with machine learning methods. The study focuses on enhancing the diagnostic capabilities of a hybrid system designed for the early detection of developing faults in the process equipment of a VVER-1000 reactor unit. A diagnostic method is proposed based on an analysis of the nature and dynamics of corrections generated by a neural network module relative to the results of physical-mathematical modeling. Diagnostic indicators-specifically, the anomaly index, non-stationarity index, and correlation anomaly index-are introduced; their joint analysis enables not only the detection of deviations but also the localization of their source. An architecture for the diagnostic module has been developed, incorporating a knowledge base of characteristic signatures associated with typical faults. The selection of indicator threshold values is statistically justified using the minimum Bayesian risk criterion and validated through ROC analysis. Results from testing on historical operational data demonstrate a detection sensitivity of 88.5%, an area under the ROC curve of 0.931, and a lead time ranging from several hours to several days. The integration of the diagnostic subsystem into the unit's information and control system-functioning as an operator advisory tool-is discussed, establishing a foundation for implementing predictive maintenance strategies.