## 1 Introduction  
The complexity of the equipment involved in modern industry has rapidly increased in the past decades [1]. Any failure of equipment may cause catastrophic consequences [2, 3], and reliability and maintenance are key for equipment [4]. Therefore, it's essential to have an effective strategy that positively coordinates scheduling and maintenance to ensure productivity, personal safety and manufacturing development [5]. Prognostics and health management (PHM) is a key technology that can guarantee the security of equipment and reduce maintenance costs [6]. As a crucial component of PHM, remaining useful life (RUL) prediction has evolved into an active research field due to its enhanced capability to determine the maintenance time [7]. RUL of equipment is defined as the length from the current time to the end of the useful life [8]. RUL prognostics approaches consist of model-based, data-driven, and fusion prognostics [9]. More particularly, model-based methods describe equipment health state by modeling the degradation evolution of physical structure. Unfortunately, model-based methods are constrained in adapting complex equipment structure [10]. Ideally, data-driven methods put to use sensor measurement data or operational data to predict RUL without foreknowledge of the physical structure and the degradation mechanism [11]. Additionally, the above-mentioned model-based methods and data-driven methods are combined into fusion prognostics methods [9]. However, very few studies about fusion prognostics methods have been reported, due to the undiscovered intricacy of the physical structure [12; 13]. Actually, data-driven methods have been proven to be capable of predicting RUL in extensive research [14; 15]. Over the years, since sensor measurement data are intrinsically of the time series nature, the mainstream data-driven research in the field of RUL prognostics focuses on prediction techniques based on sequence learning [8]. In previous research, a large number of machine learning approaches have been proposed, most of which construct prognostics models by analyzing correlative sensor sequential data and associating the discovered hierarchical patterns with a definite prognostics task [16]. These prediction models provide effective evidence to the manufacturers [17; 18] and include, for instance, auto-regressive integrated moving average-based (ARIMA) models [19; 20], hidden Markov models (HMM) [21; 22; 23], support vector regression (SVR) models [24; 25; 26], artificial neural networks (ANNs) [27; 28], radial basis functions (RBFs) [28], random forest (RF) regression [29], among others [12; 30]. Nevertheless, higher prediction demands make it unfeasible for those traditional data-driven methods to handle a growing number of complicated data [31]. In pursuit of better performance for RUL prognostics, a family of deep learning models has emerged with the ability of automatic feature extraction and high prognostics accuracy [32]. In the field of RUL estimation, a convolutional neural network (CNN) is utilized to obtain high-level spatial features from the raw sensor signals in [33; 34]. In addition, long short-term memory (LSTM) neural networks are applied to be specialized in extracting sensor temporal information [1; 8; 35; 36]. However, only spatial or temporal characteristics are considered in the above single deep learning model. Preferably, an emerging scheme combining CNN and LSTM (CNN-LSTM) has been suggested in recent research [37; 38; 39; 40; 41], most of which focuses on natural language processing, speech processing, video processing, and so forth. The applications of CNN-LSTM ensembles on prediction are countable [42]. Representative literature introducing this scheme to predict residential energy consumption can be found in [43], where raw sensor data are directly in use for prediction. For RUL prognostics, more features should be taken into account to describe equipment degradation. It is proven that the addition of a health indicator (HI) can produce accurate prediction results [13].