## 4 Results and Comparison
### Comparison with Previous Studies
#### 4.2.1 Review of Representative Solutions
Finally, the Gaussian Process Regression (GPR) method was applied on the generated CV sequential data to make the RUL prediction. [PERSON] et al. introduced a stochastic modeling approach using the EKF technique [38]. This approach adopted two types of prognostic features, one is the signal Variance and another is the Entropy within the identified signature frequency band. Once the prognostic features were extracted, an analytical function approximating the degradation process was used to initialize the parameters of EKF. The initialized EKF was then applied to the test bearing data to predict the RUL under different operating conditions. For the signal Variance feature, an exponential form of \(ae^{ht}\) was found to be a suitable modeling function, whereas a function in the form of \((a-be^{-ct})\) was used to model the time-frequency Entropy feature. Model parameters \(a\), \(b\), and \(c\) are updated with measurement data. [PERSON] et al. presented a hybrid method [39] that adopted the feature fusion strategies similar to the method by [PERSON] et al. [37]. The extracted prognostic features consist of WPD-based features and time-domain features. To fuse the prognostic features, a weighted minimum quantization error (WMQE) measure was constructed based on the trend-ability and monotonicity of prognostic feature sequences and correlations among those sequences. To predict the RUL, their method adopted the Paris-Erdogen model that is commonly used to depict fatigue crack propagation in mechanical materials. The model parameters were initialized using the MLE method and RUL was predicted using the model updated with the PF technique. [PERSON] et al. proposed a recurrent neural network (RNN) based health condition indicator (RNN-HI) [40]. Based on the similarity, monotonicity, and correlation metrics, they selected a set of prognostic features from the time-domain and time-frequency domain features similar to those by [PERSON] et al. [39]. The RNN-HI sequence was then constructed by feeding the set of selected features into the RNN model. As a hybrid approach, they described the bearing degradation process using the double exponential model \(ae^{ht}+ce^{ht}\) and adapted the PF technique for the model parameter estimation. [PERSON] et al. employed a RNN based on encoder-decoder framework with attention mechanism to predict values of HI [41]. At each time cycle, five band-pass energy values of frequency spectrum were extracted as prognostic features, which were directly into the RNN based model for HI prediction. Upon completing the HI prediction for each time cycle, final RUL value was estimated using linear regression over the up-to-date time series of HI. [PERSON] et al. also applied an RNN with LSTM modeling technique for HI prediction [42]. From the learning bearing examples, this method generated a set of representative templates of frequency spectrum for both normal and failure states. For a given test bearing, similarity-based features were calculated between new vibration data and the set of learned templates. In addition, eight typical statistical time domain features, including Mean, SD, Min-to-Max (i.e., Peak-to-Peak), Skewness, Kurtosis, Energy, Entropy, and Energy/Entropy, were also included for prognostics. These features were used to estimate HI values via a trained CNN model. Finally, the obtained values of health indicators were then extrapolated to estimate future HI of the test bearing and its RUL.