Aero-Engine health management generally involves a series of activities over the period from the aerospace breaking down until it returning to normal, including signal processing, monitoring, health assessment, decision supporting, human-computer interaction, and so on. As one of the key technology of Aero-Engine health management, fault diagnosis plays a very important role on the safe operation of Aero-Engine. Currently, for effective challenging Aero-Engine health management, a fault diagnosis of Aero-Engine based on Principal Component Analysis (PCA) s proposed. Firstly, based on a variety of significant parameters of the collected information, principal component analysis model is established. Secondly, the fault diagnosis of engine operating conditions is realized by comparing the T2 statistic and Squared Prediction Error (SPE) statistic as an engine running in good condition threshold limits. Finally, through the variable's cumulative contributions diagram with the behavior of SPE overrun, the fault variables are effectively worked out. Experimental results show that the proposed PCA method can efficiently come true Aero-Engine health management o and has some engineering applications values.