Long short-term memory neural network with scoring loss function for aero-engine remaining useful life estimation

Abstract

Estimation of the aero-engine remaining useful life (RUL) is a significant part of prognostics and health management (PHM) and the basis of condition-based maintenance (CBM) which can improve the reliability and economy. Multiple operating conditions, nonlinear degradation, and early prediction are significant and distinctive issues compared with other prognostics problems. While these issues do not get enough attention and researches in aero-engine RUL estimation. In view of these points, three specific data preparation approaches and a novel loss function are introduced. The data preparation approaches can extract high-quality data for the long short-term memory (LSTM) neural network according to the characteristic of aero-engine degradation data. Among these approaches, operating condition normalization is an effective method to handle the multiple operating conditions problems, and RUL limitation identification is a novel method to identify the turning point of the nonlinear degradation process. The scoring function is an innovative loss function used to replace the mean square error (MSE) loss function which has a preference for early prediction. The comparisons with the original LSTM and some other approaches indicate that the combination of the data preparations and the scoring loss function is an effective solution for the above issues, and can achieve the best performance among the approaches.