Automated Green Machine Learning for Condition-based Maintenance

Abstract

[Abstract]: Within the big data paradigm, there is an increasing demand for machine learning with automatic configuration of hyperparameters. Although several algorithms have been proposed for automatically learning time-changing concepts, they generally do not scale well to very large databases. In this context, this paper presents an automated green machine learning approach applied to condition-based maintenance with automatic data fusion and density-based anomaly detection based on locality sensitivity hashing. Experiments on numerical simulations of train-track dynamic interactions demonstrate the utility of the approach to detect railway wheel out-of-roundness. This unlocks the full potential of scalable machine learning, paving the way for environment-friendly systems and automated decision-making.