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

Predictive maintenance (PdM) is applied to monitor a system's life cycle to provide current diagnostics, 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.