Discriminative feature learning and selection with label-induced sparse filtering for intelligent fault diagnosis of rotating machinery

Representation learning has demonstrated its powerful potential in intelligent fault diagnosis of rotating machinery, where sparse filtering (SF) is a popular and promising representation learning method. However, the currently SF-based approaches still have two limitations: (1) discriminant ability of the learned features is insufficient when handling complex signals collected from variable operating conditions and (2) they are incapable of selecting the most relevant and important features for fault classification. To address these issues, label-induced sparse filtering (LISF) is proposed by introducing a fully-connected label layer. On the one hand, discriminant information encoded in labels is exploited in LISF's training, so discriminative ability of the learned features can be significantly enhanced. On the other hand, the importance of each feature is measured by a projection matrix, so the top-ranked relevant features can be filtered accordingly. Our method is evaluated by experiments on two case studies. It shows that LISF is able to learn discriminative features from complex signals, and achieves excellent diagnosis results over the existing advanced RL methods. Moreover, LISF can automatically select a set of a specified number of key features for fault classification, so as to improve diagnosis efficiency greatly.