Sample adaptive aero-engine gas-path performance prognostic model modeling method

An accurate gas-path performance prognostic model would provide supports for aero-engine performance assessment, maintenance plan optimization, fleet management and operation schedule determination. Three important aspects deserve further considerations in aero-engine gas-path performance prediction: (1) time series characteristics, (2) operating conditions, (3) individual-differences among the aero-engines. To deal with the aforementioned three aspects, an aero-engine gas-path performance prognostic model based on long short-term memory (LSTM) network is established for each aero-engine, which is called Single-LSTM model. The established model is able to deal with the time series characteristics and operating conditions simultaneously. Furthermore, a unified and novel prognostic model, the so-called sample adaptive LSTM neural tree (SALNT), is investigated by combining LSTM and decision trees. The developed SALNT model achieves time series characteristics extraction and hierarchical structure analysis. In the SALNT, the sample can adaptively search for the best prognostic gas-path performance model according to its own characteristics. The real-life operation data of aero-engines are adopted to compare the developed Single-LSTM model and SALNT model with several typical prognostic models in short-term prediction. The experiments show that the developed prognostic models significantly improve the accuracy and stability in short-term prediction. The SALNT eliminates the influence of individual-differences among the aero-engines in short-term prediction. Moreover, the SALNT is applied to long-term prediction. The experiments results show that the SALNT is accurate in trend prediction of gas-path performance.