# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 1 Introduction  
As a major task of condition-based maintenance (CBM), health prognostics can predictively maintain equipment, improve the reliability of the machinery and prevent catastrophic consequences [1; 2; 3; 4; 5]. The main purpose of health prognostics is to explore the on-going degradation law for the device by feature extraction of the device-related data, and then predict the remaining useful life (RUL) [6]. Consequently, RUL prediction has received more and more attention from academia and industry. RUL prediction methods can be classified into two categories: model-driven methods and data-driven methods. The model-driven methods aim to derive the degradation process of a machine by setting up mathematical or physical models [7]. With the commonly used models such as the Markov process model, the Winner process model and the Gaussian mixture model, the RUL can be predicted [8]. However, such methods rely on robust physical or mathematical models, which are difficult to obtain owing to the complex environment [9]. Data-driven methods establish the relationship between the acquired data and RUL through data mining, which can be more accurate with the real-time monitoring data. In recent years, a number of effective data-driven algorithms have been proposed and achieved good results, mainly including: neural networks [10], support vector regression (SVR) [11] and gaussian process regression (GPR) [12]. Generally, data-driven methods consist of three stages: data acquisition, health indicator (HI) construction and RUL prediction [13; 14; 15; 16]. By learning the deterioration curve of HIs, the time it takes for the current HI to reach the pre-set failure threshold (i.e., RUL) is estimated. [PERSON] et al. [17] proposed a new health indicator creation approach using binary support vector machine classifierand calculated the RUL through an identified Weibull function. [PERSON] et al. [6; 18] applied neural networks to the construction of health indicators and the RUL was obtained by the particle filter. [PERSON] et al. [19] constructed a long short-term memory-based encoder-decoder (LSTM-ED) scheme to obtain an unsupervised health indicator (HI). Generally, the prediction accuracy of RUL is seriously affected by the quality of the constructed HIs [20; 21]. The current HI construction methods are perplexed by different amplitude ranges of statistical features, which leads to unifying the failure threshold difficultly under different working conditions [9; 18]. In addition, as shown in Figure 1a, prediction methods based on HIs depend on long-term tracking of equipment from the initial to the current time, which is impractical in some cases. Hence, some scholars have paid attention to the time window (TW)-based RUL prediction method and have begun to directly establish the correlation between the sensor signals and RUL. [PERSON] et al. [22] developed a machine learning framework with a moving time window to determine the RUL of aircraft engines. [PERSON] et al. [23] used a fixed time window to predict the RUL of aircraft engines. As depicted in Figure 1b, this method can start sampling at any time when the equipment is running, obtain the data from \(t_{q}\) to \(t_{p}\), and the RUL at \(t_{p}\) can be predicted directly according to these data. The key issue of these methods is to establish the mapping between the original signal and the RUL. Recently, deep learning has made remarkable achievements in the fields of image recognition, speech recognition and unmanned driving [24; 25]. Deep learning is characterized by the deep network architecture where multiple layers are stacked in the network to fully capture the representative information from the raw input data [26]. Therefore, as a powerful tool, deep learning shows its great potential in establishing the correlation between the original signals and the RUL. [PERSON] et al. [22] created a multilayer neural network as the deep learning algorithm to build the correlation between signal characteristics and RUL. [PERSON] et al.