## 1 Introduction  
Machinery prognostics have recently emerged as an important research area because of its huge impact on the dependability, durability, and efficiency of complex systems. [PERSON] et al. [1] demonstrated in their review that there has been a considerable increase of machinery prognostics research work between 1997 and 2016. The number of publications between 1997 and 2011 amount to 576 while the total count of publications between 2012 and 2016 is 854. Along with their increasing popularity, there is also an anticipation of those machinery prognostics techniques in the industry [2, 3]. Their expected impact is meant to particularly maximize the operational availability, reduce maintenance costs, and improve system reliability and safety [1, 2]. An important application of machinery prognostics is the remaining useful life (RUL) prediction: the goal is to predict the time left before observing a failure in a given machine or system [3]. RUL prediction algorithms can be largely grouped into two main categories: data-driven methods and physics-based methods [2]. While requiring a sufficient amount of historical data, the data-driven approaches for prognostics have recently been attracting more attention given their abilities to produce generalizable solutions, which do not require extensive prior knowledge on the physical system [1,2]. In general, data driven approaches need considerable amount of data to train the degradation prediction model of the system of interest [3]. However, thanks to this age of Internet of Things and Industrial 4.0, the great amount of data that is now accessible has made the recent development or adaptation of machine learning-based RUL prediction algorithms possible. Particularly, machine learning techniques have been shown to be capable of learning to predict the RUL of systems given their vibration, sound and other physical signals. Among those machine learning methods, deep learning methods have appeared to be most promising. Deep learning has been demonstrated capable of producing impressive results in areas of computer vision, image and video processing, bioinformatics and natural language processing [4]. Given a sufficient amount of data, deep learning algorithms can build models capable of mapping complex linear or non-linear relationships between input features and output predictions to achieve satisfactory prediction performance [4]. As a result, deep learning has already been increasingly applied to RUL estimation problems [2]. [PERSON] et al. [5] built a model based on a deep fully connected architecture to predict the RUL of rolling bears using both time and frequency domain features. [PERSON] et al. [6] demonstrated that their deep recurrent neural network, their Vanilla Long-Short Term Memory (LSTM) model outperforms other recurrent neural network algorithms for RUL prediction for engineered systems. Based on the convolutional neural network architecture, [PERSON] et al. [7] proposed a novel network with modified convolution and pooling operations to predict RULs. In this paper, we are interested in one kind of RUL related system degradation monitoring problems: given a component under certain pressure, what additional pressure will lead to its break or fracture. We call this problem as remaining useful strength (RUS) prediction problem. More specifically, we are interested in the degradation prediction problem of SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials using acoustic emission as the major input signals, which is used in accident tolerant fuel holders in nuclear reactors. This material may degrade over time in their harsh service environments. Thorough knowledge of the deterioration of the SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) materials is essential to maintenance of systems built with SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\). SiC\({}_{\text{f}}\)-SiC\({}_{\text{m}}\) is a manufactured ceramic composite material that has been researched for its favorable physical attributes [8,9].