Leveraging working-condition-related features for enhanced cross-domain remaining useful life prediction of aircraft engines

Accurately predicting the remaining useful life (RUL) of aircraft engines across varying working conditions is challenging, particularly due to the absence of labeled data in the target domain. Traditional approaches in unsupervised domain adaptation (UDA) have largely focused on working-condition-invariant (domain-invariant) features, often overlooking the valuable contributions that working-condition-related (domain-related) features can provide. To address this limitation, we propose a novel UDA framework that explores the potential contributions of working-condition-related features and leverages the synergy between working-condition-invariant and working-condition-related features for cross-domain RUL prediction. Our approach involves the development of two specialized feature generators, one for working-condition-invariant features and another for working-condition-related features. We employ orthogonality constraints to optimize the expression of both feature types and ensure their independence within the feature space, which effectively reduces interference between them. Combined with a domain discriminator and a domain classifier, this setup allows our RUL predictor to harness the combined strengths of both feature types under varying working conditions. The effectiveness of our approach is validated through extensive experiments across twelve distinct working-condition scenarios, demonstrating a significant improvement in prediction accuracy, with a 9.3% reduction in root-mean-square error (RMSE).