Dual-frequency enhanced attention network for aircraft engine remaining useful life prediction

Accurately evaluating the remaining useful life (RUL) of aircraft engines is crucial for ensuring operational safety and reliability, and serves as a critical foundation for making informed maintenance decisions. In this paper, a novel prediction framework is proposed for forecasting the RUL of engines, which utilizes a dual-frequency enhanced attention network architecture built upon separable convolutional neural networks. First, the information volume criterion (IVC) index and information content threshold (CIT) equation are designed, which are applied to quantitatively quantify the degradation features of the sensor and remove redundant information. In addition, this paper introduces two trainable frequency-enhanced modules, namely the Fourier transform module (FMB-f) and the wavelet transform module (FMB-w), to incorporate physical rules information into the prediction framework, dynamically capture the global trend and local details of the degradation index, and further improve the prediction performance and robustness of the prediction model. Furthermore, the proposed efficient channel attention block generates a unique set of weights for each possible vector sample, which establishes the interdependence among different sensors, thereby augmenting the prediction stability and precision of the framework. The experimental demonstrate that the proposed RUL prediction framework can deliver accurate RUL predictions.