With the improvement of structure complexity and the strict requirement for stable operation, maintenance pattern of aircraft engine has experienced the transformation from passive response to active prevention. Ac-curate result of health evolution trend measuring is the core part of conducting the prevention maintenance. In this paper, a data-driven method based on multi-scale series reconstruction and adaptive hybrid model is pro-posed to measure the health development tendency of aircraft engine. Firstly, to quantitatively characterize the health levels of engine, a comprehensive health state index (CHSI) is innovatively constructed by an improved ensemble auto-encoder (EnAE) and self-organizing map (SeOM) neural network, which realizes the feature-level fusion of multi-source sensory data. By designing novel network structure and training pattern in EnAE, original auto-encoder model is optimized and the more robust state features can be captured from raw signals. Secondly, considering the influence of series random fluctuations on forecasting results, a multi-scale intrinsic mode functions (IMFs) reconstruction strategy using the fast ensemble empirical mode decomposition based dispersion entropy (FEEMD-DE) theory is provided to efficiently extract the regular and irregular components from original CHSI series. Finally, an adaptive hybrid model, combining a recurrent reconstructed Grey Markov (RRGM) model and long short-term memory (LSTM) network, is developed to capture the complex characteristics of recon-struction components and then complete the measurement of health evolution trend. The feasibility and supe-riority of the proposed method is validated by using a multi-source sensory dataset collected from aircraft engines, and the experimental results indicate that the measuring accuracy of the proposed method is signifi-cantly higher than that of other existing methods.
