A long-term gas-path fault diagnosis and its rapid prototype system are presented for on-line monitoring of a gas turbine engine. Toward this end, a nonlinear hybrid model-based performance estimation and abnormal detection method are proposed in this paper. An adaptive extended Kalman particle filter (AEKPF) estimator is developed and used to real time estimate engine health parameters, which depict gas turbine performance degradation condition. The health parameter estimators are then pushed into a buffer memory and for periodical renewing baseline model (BM) performance, and the BM is utilized to detect engine anomaly over its life course. The threshold in abnormal detection schemes is adapted to the modeling errors during the engine lifetime. The rapid prototyping system is designed and built up based on the National Instrument (NI) CompactRIO (CRIO) for evaluating gas turbine engine performance estimation and anomaly detection. A number of experiments are carried out to demonstrate the advantages of the proposed abnormal detection scheme and effectiveness of the designed rapid prototype system to the problem of gas turbine life cycle anomaly detection.