No abstract

No abstract

In the process of prediction and health management (PHM) about remaining useful life (RUL) of aero-engine, the state evaluation of aero-engine by constructing health index (HI) plays a very important role. At present, in the research of aero-engine state evaluation, there are still many problems: the analysis of the impact between operating conditions and sensor measurement data is insufficient, the standard for sensor data sequence selection is not established, and the HI construction is not reasonable. Aiming at these problems, firstly, a data reconstruction method is used to standardize the sensor data sequences, so as to eliminate the coupling effect between operating conditions and sensor measurement data. Secondly, different from the screening of sensor data sequences by observing the trend manually, a monotonicity standard is proposed. Then, a HI calculation method based on weighted Hellinger distance is proposed, which can construct HI more reasonably and better characterize the health condition of aero-engine. Finally, based on the standard data set of aero-engine degradation process provided by NASA in 2008, a typical HI construction method based on linear regression mapping and a similarity-based RUL prediction method are selected for comparison to verify performance of this method. The results show that the proposed method has better evaluation performance, and the HI constructed by this method is also better in RUL prediction.