Multi-Objective Feature Selection for Prognostics and Health Management Applications

Prognostics and Health Management (PHM) systems are evaluated based on different performance criteria, such as result performance and cost efficiency, which may vary depending on the application. The selected features for the PHM application can have a significant influence on the overall PHM system performance. Therefore, this work discusses an approach for multi-objective feature selection for PHM, aiming to optimize overall PHM system performance regarding different criteria. A regularization based embedded approach is tested, which incorporates external evaluation factors for the features in the loss function calculation. The monetary cost of each feature is used to optimize a PHM system for two criteria: Accuracy and cost efficiency. A real test dataset from a monitoring application of a process thermostat is used for evaluation of the approach. It is shown that the presented approach can aid to select suitable subsets of features for multi-objective problems. It can assist in identifying relationships in the dataset and can reduce the dependency on domain knowledge.