“…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].…”