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 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 was evaluated on turbine engine datasets. Experimental results show that (1) compared with other feature selection methods, the root mean square error (RMSE) of the proposed method is reduced by 12.3%; (2) compared with other RUL prediction methods, the RMSE of the proposed method is smaller than most methods but without a complicated training process. The results demonstrate that the proposed method achieves highly competitive prediction performance. By employing the proposed method, it is possible to significantly reduce the risk of engine failures, thereby improving safety and economic efficiency.