Anticipating system failures using predictive strategies based on efficient prognostics has become an important topic in manufacturing where maintenance plays a crucial role. As such, promising prognostics approaches use data-driven machine learning techniques, though the initial data set for learning is often small as failure occurrences are rare. Therefore, this study investigates data augmentation methods for improving prognostics by increasing data set size using samples generated by altering existing ones. First, a method is proposed for quantifying the gain from additional data. Thereafter, augmentation methods are assessed through a benchmark. Finally, contributions are illustrated in a steel industry case-study.