  Condition-based predictive maintenance can significantly improve overall
equipment effectiveness provided that appropriate monitoring methods are used.
Online condition monitoring systems are customized to each type of machine and
need to be reconfigured when conditions change, which is costly and requires
expert knowledge. Basic feature extraction methods limited to signal
distribution functions and spectra are commonly used, making it difficult to
automatically analyze and compare machine conditions. In this paper, we
investigate the possibility to automate the condition monitoring process by
continuously learning a dictionary of optimized shift-invariant feature vectors
using a well-known sparse approximation method. We study how the feature
vectors learned from a vibration signal evolve over time when a fault develops
within a ball bearing of a rotating machine. We quantify the adaptation rate of
learned features and find that this quantity changes significantly in the
transitions between normal and faulty states of operation of the ball bearing.
