## 1 Introduction  
Health state prediction in complex equipment or subsystems based on conventional methods such as physical modeling approaches became a very difficult task. Due to the need for deep knowledge of system components and their inner interactions, the modeling process complexity increased and became almost impossible. Even if the final model can be prepared under limited conditions, results might be misleading the prediction or not accurate enough due to poor generalization. Nowadays, machine learning applications for Remaining Useful Life (RUL) estimation has received growing importance due to the availability of heterogeneous data, which in turn motivated researchers to boost the conventional RUL prediction paradigm with a variety of prediction approaches. Continuous improvements of machine learning models make its use in Prognostic Health Management (PHM) more relevant [1]. It allows modeling systems behavior by only extractingimportant patterns from their retrieved data even without a prior knowledge of their internal characteristics. Unlike conventional paradigms, machine learning techniques aims to reduce modeling complexity with less human intervention and less computational costs. For instance, [PERSON] et al. [2] used a Hidden Semi Markov Model (HSMM) to train SVM for RUL prediction. [PERSON] et al. [3] designed a hybrid SVM-based swarm intelligence technique searching for the best training coefficients during RUL prediction of aircraft engines. [PERSON] et al. [4] combined Spectral Kurtosis (SK) to SVM to be able to construct an RUL predictor with a more meaningful feature mapping for wind turbine high-speed shaft bearings health prognosis. [PERSON] et al. [5] based on a hybrid model constructed a training formwork of the ELM model. They adopted a time window features scaling as a preprocessing and appropriate features selection step to guarantee an accurate prediction of RUL. [PERSON] et al. [6] proposed a hybrid autoregressive model combined with an improved SVM using genetic algorithm to build several estimation algorithms for early RUL prediction. [PERSON] et al. [7] used a SVM-based similarity approach for RUL prediction with the same C-MAPPS dataset. It should be mentioned that all cited works could be classified as a hybrid model that aims to accurately predict the RUL by an unsupervised training or preprocessing of training data before a \"single-batch\" supervised training. However, time-varying data and parameter update is not considered in these cases, which is incompatible with data-driven prediction models able to address dynamic health deterioration of equipment according to internal or environmental conditions. Neural networks with mini batch training are able to interact with flowing sequences of data and update the prediction model to satisfy changes in newly arrived ones. In [8], a new data-driven approach is introduced by [PERSON] et al. by training a Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network with Weibull Distribution (WD) to avoid time domain fluctuation during RUL prediction. [PERSON] et al. [9] developed a mini batch hybrid deep NN that takes two paths for RUL estimation; the multidimensional feature extraction based on Long Short Term Memory (LSTM) and convolutional NN and the prediction path via a fusion algorithm. [PERSON] et al. [10] constructed feature mapping and training schemes based on ensemble residual CNN and validated their training model with the K-fold cross-validation method after constructing several learners to predict RUL accurately. [PERSON] et al. [11] simplified the RUL prediction process of aircraft engines by the direct use of raw sensors measurements without a prior expertise in signal processing using a novel deep convolutional NN combined with a time window feature extractor. The prediction approaches used in these works are based on mini batch training interacting with data-driven sequences.