A data-driven degradation prognostic strategy for aero-engine under various operational conditions

Abstract

Abstract Practical degradation prognostics for aero engines is difficult owing to the degradation features covered by the continuous switching among various operational conditions. A novel degradation prognostics strategy for aero engines under various operating conditions is proposed in this study. Specifically, to remove the influence of different operational conditions, degradation features, which are hidden in raw data, are extracted. This strategy can realize health state estimation, degradation trend prediction and remaining useful life (RUL) estimation. First, a k-means algorithm and three defined indicators are combined to distinguish different operational conditions, extract preferably monotonic and trendable degradation features. A linear logistic regression model is utilized to construct a synthesized health index (SHI) library. Second, a deep forest classifier (DFC) and long short-term memory (LSTM) are utilized to establish an offline health state estimation model and a degradation trend prediction model. Finally, a dynamic time warping algorithm (DTW) is adopted to obtain a new SHI for online RUL estimation based on the two aforementioned offline models. Verification results using the NASA Prognostics Center dataset show that the proposed strategy for aero engines under various operating conditions is effective and feasible.