Residual-based adversarial feature decoupling for remaining useful life prediction of aero-engines under variable operating conditions.

Accurate remaining useful life (RUL) prediction holds significant importance for health management of aero-engines, ensuring safety and reducing the maintenance cost. The coupling between variable operating conditions and diverse sensor signals makes it hard to construct an accurate and stable model, which predicts precise results and simultaneously obtains condition-independent features reflecting the degradation trend. To address the problem, this paper proposes a novel approach named residual-based adversarial feature decoupling (RAFD) for RUL prediction of aero-engine which achieves decoupling both explicitly and implicitly. The paper formally defines the coupling relationships within the signals, consisting of information from normal pattern, degradation pattern, and operating conditions which are decoupled explicitly and implicitly. An operation-condition-mapping model (OCMM) is developed to explicitly decouple normal pattern, establishing the mapping between operation conditions and sensor signals in the healthy stage. Residuals serve as inputs of the prediction model, a continuous adversarial neural network specifically designed for implicit decoupling which extracts condition-independent features and predicts RUL accurately. The effectiveness of our method is validated on NASA's N-CMAPSS dataset, resulting in superior prediction performance and feature visualization when compared with other existing methods. • Signal coupling is defined and decoupled by RAFD explicitly and implicitly. • OCMM is proposed to extract residuals for explicit degradation indication. • CANN is proposed to capture implicit decoupling of operation conditions. • The superiority of the method is validated on N-CMAPSS dataset. [ABSTRACT FROM AUTHOR]