bda-final RUL Prediction with Random Forest Regression on CMAPSS FD004 dataset Piecewise RUL degeneration function with early RUL = 125 Classification of operating conditions K-Means for fault modes target of the train set and SVM for fault modes classification of the test set Random forest regression for RUL prediction 2 baseline models: RF regression with raw sensors' signal Decision tree regression with the same feature engineering Fault Classification with SVM on CWRU dataset Wavelet packet decomposition for signal processing and energy for featuring engineering SVM for classification