## 2 Classification of Prognosis Approaches  
To date, there are a number of literature reviews on the prognosis approaches for rotating machineries [18; 19; 20; 21]. For example, [PERSON] et al. [18] reviewed prognosis methods for critical components, such as the bearing, gear, shaft, pump, and alternator. The authors classified the prognosis approaches into three types, namely model-based, data-driven, and hybrid prognosis approaches. However, the classification does not include complete methods on each prognosis approach. In their work,two methods are classified as a model-based approach, i.e., an alpha-beta-gamma tracking filter and Kalman filter, while a neural network (NN), fuzzy logic, and a decision tree are classified as a data-driven approach.  
Another review paper by [PERSON] et al. [19] presented a clearer classification in prognosis approaches, but the review placed a greater emphasis on rotating machinery diagnostics than machinery prognostics. The authors classified the prognosis approaches into three groups: statistical, artificial intelligent (AI), and model-based approaches. The statistical approaches include statistical process control (SPC), logistic regression, autoregressive and moving average (ARMA), proportional hazard model (PHM), proportional intensity model (PIM), and hidden Markov model (HMM). In AI techniques, e.g., an artificial neural network (ANN) and its sub-classes, such as self-organising neural networks, dynamic wavelet neural networks, and recurrent neural networks, back propagation neural network and neural-fuzzy inference systems are still commonly used in AI prognostics. Among model-based approaches, defect propagation models via mechanistic modelling and the crack growth rate model are the commonly used methods.  
A popular review paper on prognosis methods was presented by [PERSON] et al. [21]. The authors classified the methodologies for predicting rotating machinery failure into two different groups, namely physics-based and data-driven prognosis models. A number of papers focusing on physics-based prognostics which used [PERSON] formula are still found to be dominant [21]. Other methods, such as finite element analysis (FEA) to calculate stress and strain field, and the Forman law of linear elastic fracture mechanics, are also classified in physics-based approaches. Similar to the result from the two review papers previously mentioned, ANN and its variants are currently the most commonly used methods in the data-driven prognosis class. Other methods, such as fuzzy logic, regression analysis, particle filtering, recursive Bayesian technique, and HMM, are also included in data-driven prognosis methods. However, a number of methods within data-driven methods, as presented in [21], need further sub-classification in terms of artificial intelligent or statistical approaches.  
A recent review is presented by [PERSON] et al. [22], which mentioned that a machinery prognostic method generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health state (HS) division, and RUL prediction. In addition, they also explained that the existing research work and literature review have converged to the four processes, especially the latter one. The paper presents a systematic review that comprehensively covers the four technical processes.  
Four prognosis approaches: (1) model-based approaches; (2) reliability-based methods and probability models; (3) data-driven approaches; and (4) combined data-driven approaches and reliability-based methods, have been developed and presented in the literature. Brief information on the prognosis methods of each approach, including the method's name and feature, is presented in Table 1. It can be seen that RMS and kurtosis are the most commonly used features in prognosis methods. It is worth noting that the reviewed methods in this paper only focused on rolling element bearings prognostics. A detailed explanation of prognosis methods classified in four approaches can be seen in [23].