Knowledge and data jointly driven aeroengine gas path performance assessment method

Aeroengines, as the sole power source for aircraft, play a vital role in ensuring flight safety. The gas path, which represents the fundamental pathway for airflow within an aeroengine, directly impacts the aeroengine's performance, fuel efficiency, and safety. Therefore, timely and accurate evaluation of gas path performance is of paramount importance. This paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method, combining Fingerprint and gas path parameter deviation values. Firstly, Fingerprint is used to correct gas path parameter deviation values, eliminating parameter shifts caused by non-component performance degradation. Secondly, coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding window. Thirdly, an Ensemble Empirical Mode Decomposition and Non-Local Means (EEMD-NLM) filtering method is designed to “clean” data noise, completing the preprocessing for gas path parameter deviation values. Afterward, based on the characteristics of gas path parameter deviation values, a Dynamic Temporary Blended Network (DTBN) model is built to extract its temporal features, cascaded with Multi-Layer Perceptron (MLP), and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder (DTB-AutoEncoder). Eventually, by training this improved autoencoder, the aeroengine gas path multi-component performance assessment model is formed, which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values, thereby achieving the performance assessment of engine gas path components. Through practical application cases, the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.