CUR-Estimator: Towards reliable missing data imputation for aero-engine degradation process

Abstract

In the process of missing data imputation for aero-engine life cycle degradation time series, two primary challenges arise. First, due to variations in the duration of different flight missions, and the fact that the same flight phase may also vary in duration across different flights, the time intervals for collecting key samples are not always consistent. This variability increases the difficulty of evaluating the impact of individual flights on overall performance changes. Second, when using neural networks for imputing missing data, issues such as significant noise or extended periods of missing data may arise, leading to unreasonable imputation results. To address the challenges, this paper proposes a Constrained Unseen Recovery Estimator (CUR-Estimator) for imputing missing data in aero-engine life cycle degradation datasets. Firstly, the time interval information is encoded via a transformer-enhanced gate recurrent unit. The results are then combined with missing masks to adjust the hidden states and input weights for the missing segments, forming the Interval-Aware Temporal Imputation Network. Secondly, this paper uses statistical interpolation methods to constrain the imputation results of the neural network, limiting the range of imputation outcomes and thereby reducing the possibility of unreasonable outputs. As an example, the Piecewise Cubic Hermite Interpolating Polynomial is applied to constrain the Interval-Aware Temporal Imputation Network in handling time interval information. Finally, experiments were conducted using a simulation dataset and a real civil aero-engine dataset, which showed that the proposed method has high accuracy and strong stability.