For using online data to diagnose the degradation status of commercial aero-engine components, an aggregated method including engine steady state modeling and unscented Kalman filter (UKF) was proposed. First, the method represents the general characteristic curves of components with nonlinear multivariate functions with respect to their working conditions, and uses machine learning methods to confirm function coefficients that fit the observed data best, subsequently acquires the steady working model of aero-engines. Then, by analysis of individual degradation properties and effects on entire engine performance of components, the key degradation factors of components were defined. Moreover, the standard UKF process was modified, by adding moving window sampling strategy to overcome low-observability problem, and replacing standard observation equation with combined model of engine steady state model and engine working conditions. At last, the acquired filter was used to process observed engine data to track the trends of component degradation factors. As indicated by the test on commercial engine dataset, the proposed method can conveniently diagnose the health status of engine components once the observed report returns to on-ground data centers, and the acquired results coincided well with the internal damage information of engines collected by visual inspection. The proposed method performed well in overcoming modeling nonlinearity, low-observability, and high data noise problems in aero-engine performance diagnosis and showed to be applicable to industrial conditions.