A relative entropy based feature selection framework for asset data in predictive maintenance

•High-dimensional asset data limit the performance of machine learning algorithms.•Features that measure an asset’s condition are characterized per ability to capture fault implications.•General feature engineering methods should not consistently adequate for raw asset data.•Feature selection method applied to C-MAPSS dataset.