Feature Engineering Toolkit for Predictive Analytics in Engineering and Healthcare Informatics

This paper presents a feature engineering toolkit that includes complexity-based and recurrence-based feature extraction methods, along with dependency-focused and importance-focused feature selection methods. The toolkit is designed to extract information with predictive power from sensor data, thereby enhancing the capability of diagnostics, prognostics, and health management (PHM). Complexity-based features measure randomness and dynamic variations within the sensor data, while recurrence-based features identify recurring patterns. Dependency-focused and importance-focused feature selection methods improve model performance by identifying the most relevant features for given tasks. This paper demonstrates the toolkit on four applications: wind turbine bearing condition classification, hydraulic systems condition monitoring, CNC tool wear estimation, and respiratory health monitoring. The analytical results exhibit robust prediction accuracy and improved result explainability.