Feature Selection in the Context of Prognostic Health Management

In the chemical processing industries, sensors for pumps are among the most commonly used machinery. Condition-based maintenance (CBM) and prognosis health management (PHM) determine the most cost-effective time to overhaul pumps. In order to determine the status of the pump, a signal-emitting accelerometer is employed. Stationarity-based feature extraction from amplitude signals is used to process the signal. Utilizing the time-domain function, multiple statistical results were produced. Eight fault codes were classified using support vector machine method. The enormous amount of data points necessitated the use of feature selection. In terms of accuracy, precision, recall, and F1 score, the Chi-square feature selection method exceeds other approaches.