Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture

Abstract

Remaining useful life (RUL) estimation has been intensively studied, given its important role in prognostics and health management (PHM) of industry. Recently, data-driven structures such as convolutional neural networks (CNNs), have achieved outstanding RUL prediction performance. However, conventional CNNs do not include an adequate mechanism for adaptively weighing input features. In this paper, we propose a double attention-based data-driven framework for aircraft engine RUL prognostics. Specifically, a channel attention-based CNN was utilized to apply greater weights to more significant features. Next, a Transformer was used to focus attention on these features at critical time steps. We validated the effectiveness of the proposed framework on benchmark datasets for aircraft engine RUL estimation. The experimental results indicate that the proposed double attention-based architecture outperformed the existing state-of-the-art (SOTA) algorithms. The double attention-based RUL prediction method can detect the risk of equipment failure and reduce loss.