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The health of the aircraft engines is of great concern. And it is a key task to predict the remaining useful life (RUL) of the aircraft engines accurately. However, there are still challenges in RUL prediction, such as the flaw in incomplete sensor signals acquired, difficulty in determining the importance of sensor signals, and neglect of the key time points with significant performance degradation information in the sensor signals when performing RUL prediction. To tackle these challenges, a dual attention framework named Channel Attention & Temporal Attention based Temporal Convolutional Network (CATA-TCN) is proposed for the RUL prediction of the aircraft engines. Specifically, channel attention is integrated into TCN to focus on sensor signals with critical impact on RUL prediction and suppressing unimportant ones in long-term horizon. Next, the processed sensor signals are fed into temporal attention module, which enhances the impact of the key time points and generates the critical degradation features. Finally, the CATA-TCN outputs the predicted RUL by performing non-linear mapping on the extracted features. Turbofan engine degradation simulation data set (C-MAPSS dataset) and real flight data are used to validate the CATA-TCN framework. The experimental results show that the proposed method is significantly more accurate on overall prediction performance (Score and RMSE) than other state-of-the-art methods, especially under changeable operation conditions and complex fault modes. [ABSTRACT FROM AUTHOR]

The Prognostics Health Management (PHM) of modern equipment typically employs Remaining Useful Life (RUL) prediction to assess health status. Existing mainstream RUL prediction methods have improved prediction performance by adopting data-driven approaches based on deep learning. However, most methods overlook the impact of noise interference caused by the redundancy of sensor data across channels, leading to a decrease in RUL prediction performance. Furthermore, these methods lack transparency and interpretability, which are crucial for maintenance personnel in accurately diagnosing the degradation process of equipment RUL. Therefore, we propose a Factorized temporal-channel fusion and Feature fusion based Variational Encoding (FFVE) for interpretable RUL prediction. By utilizing the factorization operation, we construct the Factorized Temporal-Channel Fusion (FTCF) block to learn temporal and channel dependencies, thereby reducing redundancy between channels. Through the feature fusion operations which mix original information with extracted feature information, we augment the original information that is lost during the deep network learning process, consequently avoiding performance degradation caused by increasing network depth. Through the above encoding process, sensor data is effectively compressed into a 3D latent space for predicting and interpreting the equipment degradation process. Extensive experiments were conducted on two datasets of the C-MAPSS turbofan aircraft engine and the NASA lithium-ion battery. The results demonstrate that our method outperforms state-of-the-art methods in terms of prediction accuracy. Additionally, our study provides a novel visual means of assessing turbofan engine health status, elucidating the engine RUL degradation process learned by our method. [ABSTRACT FROM AUTHOR]