# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
###### Abstract  
Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results, there are still some challenges. For example, the spatial features and importance differences hidden in the raw monitoring data are not sufficiently addressed or highlighted. In this paper, a novel multi-head self-Attention fully convolutional network (MSA-FCN) is proposed for predicting the RUL of turbofan engines. MSA-FCN combines a fully convolutional network and multi-head structure, focusing on the degradation correlation among various components of the engine and extracting spatially characteristic degradation representations. Furthermore, by introducing dual multi-head self-attention modules, MSA-FCN can capture the differential contributions of sensor data and extracted degradation representations to RUL prediction, emphasizing key data and representations. The experimental results on the C-MAPSS dataset demonstrate that, under various operating conditions and failure modes, MSA-FCN can effectively predict the RUL of turbofan engines. Compared with 11 mainstream deep neural networks, MSA-FCN achieves competitive advantages in terms of both accuracy and timeliness for RUL prediction, delivering more accurate and reliable forecasts. remaining useful life; turbofan engine; deep learning; deep neural network +
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can precisely understand degradation trends and rules. In addition, since the equipment used in different application scenarios has unique degradation mechanisms, it is difficult to apply a model built for one case to the same equipment in other applications. Therefore, the disadvantage of the model-based method is that it is less practical and flexible. With the rapid advancement in sensor technology and signal processes, data-driven methods have gradually become a research hotspot in the field of RUL prediction. Data-driven methods extract degradation features and information from the massive raw monitoring data gathered by diverse sensors installed on the equipment, aiming to establish the mappings between the monitoring data and corresponding RUL. Data-driven methods based on artificial intelligence have become increasingly popular, mainly including methods based on conventional machine learning (CML) and deep learning (DL). CML-based approaches use shallow machine learning algorithms for feature learning and regression to obtain degradation information from monitoring data and create the corresponding mappings to achieve the goal of RUL prediction. [PERSON] and [PERSON] [4] proposed an optimal Gaussian process regression approach to predict the RUL of slow speed bearings. [PERSON] et al. [5] combined support vector regression with the ISOMAP feature reduction technique to predict the RUL of bearings. [PERSON] et al. [6] utilized random a forest model for tool wear prediction. [PERSON] et al. [7] proposed a prognostic model combining the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to predict the RUL of turbofan engines. [PERSON] et al. [8] proposed a hidden semi-Markov model-based approach for the RUL prediction of air-handling systems and their components. The performance of CML-based methods relies heavily on feature extraction and selection, which requires strong support from domain experts and expertise about the related equipment. Deep learning-based RUL prediction methods [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] can automatically learn various features and deep-level representations from a large amount of raw data through deep neural networks, which effectively overcomes the shortcomings of CML-based approaches. In the field of RUL prediction for tools, [PERSON] et al. [20] proposed a dual-module methodology framework that integrates the following two core modules: detection and regression.