Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks  
[PERSON]  
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[PERSON]  
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[PERSON]  
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[PERSON]  
2 Information Technology Construction Management Center, Kunming University of Science and Technology, Kunming 650504, China  
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[PERSON]  
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RUL prediction technology is a important step in implementing PHM, and therefore, predicting the RUL of engines holds significant research significance and practical value in ensuring flight safety and reducing operational costs for an aircraft. Despite the rapid development of RUL prediction technology in the aviation industry, it still faces several challenges, such as difficulty in extracting effective features from vast amounts of engine raw monitoring data and issues with low accuracy in predicting engine remaining lifespan. In conclusion, integrating advanced domain knowledge with innovative technologies to continually improve the practicality of engine RUL prediction remains a key challenge in the field of aviation maintenance. Currently, engine RUL prediction methods include empirical methods, physics-based failure model methods, and data-driven methods [3]. Prediction methods based on physical failure models require utilizing the interactions among internal physical processes and components of the engine to establish mathematical models. However, constructing the physical model presents significant challenges [4]. Empirical prediction methods [5] rely on expert industry knowledge and involve analyzing the engine's operational status and fault data to generate predictions. While simpler, these methods often yield lower accuracy in RUL prediction. Data-driven prediction methods [6] primarily rely on extensive raw engine monitoring data. They use advanced computer algorithms to uncover patterns in the data and construct predictive models for RUL estimation, achieving higher prediction accuracy. Compared to the other two methods, data-driven approaches are more versatile. Although early machine learning methods such as random forest [7], support vector machine [8] and Markov models [9] have shown good capabilities in data processing, their relatively simple structures make it difficult to fully explore the hidden relationships among data, resulting in lower prediction accuracy. In contrast, deep learning methods can learn abstract feature representations of data, which give them an advantage in handling long-term data dependencies and complex dynamic patterns. In the realm of deep learning methods, [PERSON] and others [10] have proposed a recurrent neural network (RNN) prediction method based on probabilistic Bayesian principles to address the uncertainty factors in RUL prediction. [PERSON] et al. [11] were the first to apply convolutional neural network (CNN) methods to RUL prediction problems, enabling automatic learning from raw signals. [PERSON] et al. [12] developed a bidirectional long short-term memory (LSTM) neural network prediction model to assess engine remaining life. [PERSON] et al. [13] used 1D-CNN to map multiple state variables' long-term sequences directly to the engine's remaining life, achieving more accurate RUL values.