Semi-supervised classification for rolling fault diagnosis via robust sparse and low-rank model

Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. However, the insufficiency of labeled samples is major problem for handling fault diagnosis problem. To address such concern, we propose a semi-supervised method for diagnosing faulty bearings by utilizing unlabeled samples. The superiority of our algorithm has been validated by comparison with other state-of art methods based on a rolling element bearing data. The classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis.