An improved similarity-based RUL prediction method considering degradation degree of multiple condition monitoring parameters for aero-engines

Abstract The aero-engine is the heart of an airplane. Predicting the remaining useful lifetime (RUL) of an aero-engine bears great significance, not only for improving the reliability and safety of the aero-engine but also for ensuring aircraft safety and performance. However, both issues, namely the selection of uncorrelated parameters for RUL estimation and the lack of a standard theoretical methodology for Health Index (HI) construction, inevitably impact the prediction accuracy. Here, we proposed an improved similarity-based RUL prediction method considering the degradation degree of multiple condition monitoring parameters for aero-engines. This method includes the improved Minimum-redundancy Maximum-relevancy (mRMR) approach for the quantitative selection of key parameters, and the similarity matching approach which takes into account the degradation degree of multiple parameters instead of constructing HI. The effectiveness of the proposed method is evaluated on publicly available turbine engine datasets. The results demonstrate that the proposed method achieves highly competitive prediction performance and gives more robust and reliable RUL estimations. By employing the proposed method, it is possible to significantly reduce the occurrence of accidents and losses related to engine failures, thereby enhancing safety and economic efficiency.