  As connected sensors continue to evolve, interest in low-voltage monitoring
solutions is increasing. This also applies in the area of switchgear
monitoring, where the detection of switch actions, their differentiation and
aging are of fundamental interest. In particular, the universal applicability
for various types of construction plays a major role. Methods in which
design-specific features are learned in an offline training are therefore less
suitable for assessing the condition of switchgears. A new computational
efficient method for intelligent online feature selection is presented, which
can be used to train a model for the addressed use cases on-site. Process- and
design-specific features can be learned locally (e.g. on a sensor system)
without the need of prior offline training. The proposed method is evaluated on
four datasets of switchgear measurements, which were recorded using
microelectromechanical system (MEMS) based sensors (acoustic and vibration).
Furthermore, we show that the features selected by our method can be used to
track changes in switching processes due to aging effects.
