# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 1 Introduction  
The aero-engine, as the main power source, is the core component of various spacecraft. Its safety, reliability and economy are the focus of aero-engine manufacturers, maintenance plants and other relevant departments [1]. As an important reference index to achieve preventive maintenance, it is of great significance to accurately estimate the RUL of the aero-engine. However, the working environment of the engine is harsh (high temperature, high pressure, etc.), and existing prediction methods often cannot meet the industrial demand.  
With the rise of artificial intelligence, a new maintenance concept known as Condition Based Maintenance (CBM) has been seen a lot in recent research. Compared with traditional maintenance measures like scheduled maintenance, CBM is more efficient and costs less time [2, 3]. This situation is also reflected in the study of the RUL estimation. Traditional prediction methods are built based on the physical mechanism of equipment or system degradation [4], such as particle filter [5], Eyring model [6], Weibull distribution [7], etc. This shows an over-reliance on expert knowledge. Besides, for aero-engine, obtaining the proper physical and mathematical model is challenging for the actual industrial environment. In contrast, CBM attempts to detect the degradation law in a data-driven way [8, 9]. It takes monitoring data collected by the sensor as input. Then, the mapping between data and labels could be learned with some models. Finally, the RUL estimation could be realized by this trained model. Hence, the RUL estimation based on CBM is easier to implement in theactual industrial environment. With the increase in the amount of data and the continuous learning of the model, the prediction effect is more accurate. The typical data-driven methods include Support Vector Regression (SVR) [10] and Hidden Markov models [11]. In recent years, artificial neural networks have been introduced to replace feature extraction in the data-driven method, such as Multi-layer Perceptron (MLP) [12], Deep Belief Networks (DBN) [13], CNN [14], Recurrent Neural Networks (RNN) [15], LSTM [16], etc. LSTM in particular, a variant of RNN, is one of the most popular neural networks to solve time series problems. It could effectively alleviate the gradient vanishing which often occurs in RNN training. However, LSTM needs to iterate in time steps, which cannot be parallel. This means that the prediction model with the LSTM layer is inefficient in the face of real-time prediction of big time-series data. Recently, one-dimensional CNN has been applied to deal with sequential problems and achieved good results in some studies [17]. Compared with LSTM, CNN can be parallelized effectively, benefiting by its convolution structure.  
In the paper, a more efficient neural network is proposed to estimate the RUL with CAE and TCN. There is no LSTM layer in the model structure, instead of the convolution layer. Therefore, the algorithm can greatly improve the prediction efficiency of the RUL estimation without losing the prediction accuracy. In addition, a state discriminator based on ExtraTreesClassifier is used to label the RUL in the data preprocessing. Combined with tree model interpretation, the automatic filtering of input features is realized.  
The rest of this paper is structured as follows. Section 2 defines the structure of the proposed prediction model. In addition, the procedure of the whole RUL estimation is described. ExtraTreesClassifier is used as the state detector and K-means++ as the new operational features extractor. Then, in Section 3, an experiment is described with the CMAPSS datasets. The results are compared with widely used prediction algorithms, vanilla LSTM and one-dimensional CNN. Finally, a summary of this paper is provided with conclusions in Section 4.