A Data-Driven Approach for Assessing Aero-Engine Health Status

Abstract

Aero-engine health assessment is an important issue in the prognostics and health management (PHM) field. To improve the accuracy of health assessment, a novel data-driven approach is proposed in this paper. Unlike the model-based methods, the proposed data-driven method does not require to know the physical nature of degradation mechanism. Also, it is different from traditional data-driven methods and does not require to construct the synthesized health index or pre-set the health thresholds. In this work, the proposed method consists of feature selection, health state division and health state evaluation. Firstly, Fisher's discriminant ratio (FDR) is proposed to evaluate the sensor-based condition monitoring information, so as to eliminate the redundant information while retaining dominant information. Secondly, to solve the problem of unlabeled observation data, fuzzy c-means (FCM) clustering algorithm is presented to define the aero-engine health states. Finally, a bidirectional long short term memory (Bi-LSTM) network, which can capture the bidirectional long-range dependencies of features, is introduced to construct the health state evaluation model. Experimental results on aero-engine data set from NASA indicate that the proposed method can effectively improve the accuracy of health assessment, which can inform maintenance-related decisions.