A Relative Entropy based Feature Selection Framework for Asset Data in Predictive Maintenance

Abstract Predictive maintenance (PdM) is applied to monitor a system’s life cycle to provide current diagnostics and prognostics, and provide information capable of guiding maintenance related decisions. Often, an asset’s life cycle is monitored using multiple measurements which translate to high-dimensional (multivariate) data. The large volume of data used to describe an asset’s life cycle has led to current state-of-the-art data-driven PdM relying on machine learning (ML). As research shows, high-dimensional data diminish ML algorithm performance. Generally, high-dimensionality is managed by feature engineering, except asset data characteristics differ from characteristics managed in typical feature engineering problems. In data-driven PdM, information regarding observed faults in an asset is important. Such information is often misinterpreted or lost when general feature engineering is performed on asset data. This work proposes a correlation and relative entropy (C-RE) feature engineering framework specific to asset data. C-RE, applies correlation based hierarchical clustering and relative entropy through the measure of Kullback-Leibler divergence to generate a lower-dimensional feature subset of the original data. The resulting feature subset has minimal redundancies and the highest content of domain-specific information relating to the influence of faults observed during an asset’s life cycle. The utility of C-RE is demonstrated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset which describes the run-to-failure life cycles of multiple aircraft engines.