Feature Extraction Methods for Prognosis Maintenance Model

Research in prognosis maintenance, a branch of condition-based maintenance has received more attention from researchers lately. They focus on predicting when is the most suitable time to perform maintenance. Our review suggests that investigation on feature extraction in development of prognosis prediction model is still limited. This paper presents our study to find the most effective method for features extraction from maintenance monitoring data. The chosen features set should effectively improve the prognosis maintenance model performance. There have been several investigations to study feature extraction methods; however, the appropriate one is yet to be identified. In this research, we used datasets publicly available from National Aeronautics and Space Administration (NASA) army research laboratory. These datasets were generated through a simulation of the turbofan engine by using Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software developed by NASA army research laboratory. Features extraction methods such as correlation among sensors, correlation among the outputs, variable weighing and treated data methods were studied in this research. Next, the extracted features were applied to the regression tree for searching an appropriate prognosis model. Based on the Remaining Useful Life (RUL) prediction results, the correlation among sensors method was found as the best method that can represent the most useful features for the prediction model.