Abstract: Data-driven method is used to evaluate the health status of the key components in the aircraft main power system. The rectifier diodes in the rotating rectifier and the metal oxide semiconductor field effect transistor(MOSFET) in the voltage regulator are selected as the key components of the aircraft main power system with high failure rate, and their aging characteristics are analyzed and determined. Using the simulation data, the average excitation voltage and the average excitation current of the exciter are selected as the characteristic variables of the power system to represent the health status of the key components of the system. Principal component analysis(PCA) is used to decouple the data of system characteristic variables, and the characteristic parameters which can represent the aging status of two key components are obtained. The Gaussian mixture model is used to establish the health reference model of key components, and the Mahalanobis distance between the system aging model and the health reference model is calculated. The Mahalanobis distance data is processed by K-means clustering analysis to obtain the data training sample set containing the health status level information of key components, and neural network is used to train the data of the sample set to obtain the health status evaluation classification functions of the key components of the system. Monte Carlo simulation is used to obtain a large amount of data to verify the classification function. The matching degree between the original data and the evaluation results of the classification function of different health status level data of key components is more than 90%, which shows the effectiveness of this health assessment method.