Similarity-based probabilistic remaining useful life estimation for an aeroengine under variable operational conditions

Abstract

System-level remaining useful life (RUL) estimation is difficult due to multiple degrading components, external disturbances, and variable operational conditions. A similarity-based approach does not rely on health assessment and is more suitable for system-level RUL estimation. However, for practical applications, how to capture effective degradation features from raw data, how to fuse multiple nonlinear sensor data, and how to handle multiple source uncertainties need to be considered. To solve the above challenges, this study focuses on RUL estimation for systems under variable operational conditions. A similarity-based probabilistic RUL estimation strategy is proposed and verified using the NASA aeroengine dataset. First, measurement uncertainty can be addressed. Proper degradation features are extracted by three defined indicators. Subsequently, multiple nonlinear sensor data fusion and unsupervised synthesized health index construction can be realized using the proposed deep autoencoder-based polynomial regression approach. Finally, this strategy can handle the modeling and prediction uncertainties, including providing probabilistic RUL estimation results by well-trained residual-based similarity models. The verification results indicate the effectiveness and feasibility of the proposed strategy.