Abstract Representation learning has demonstrated its powerful potential in intelligent fault
diagnosis of rotating machinery, where sparse filtering (SF) is a popular and promising …

Sparse filtering (SF), as a recently emerging unsupervised feature learning method, has
drawn much attention in intelligent fault diagnosis of rotating machinery. Generally, SF …

Fault diagnosis based on artificial intelligence methods is a promising tool to eliminate
reliance on a priori knowledge. Sparsity is an increasingly important topic in the field of …

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing
to its efficiency in extracting discriminative features automatically. Sparse filtering (SF) is a …

Early fault feature extraction and fault diagnosis are of great importance for predictive
maintenance of rotating machinery. To accurately extract early fault features from original …

Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are
labor-intensive and time-consuming. Among various unsupervised feature extraction …

In the era of data deluge,“big data” generated by mechanical equipment creates higher
requirements for the field of mechanical fault diagnosis. Intelligent diagnosis methods have …

Rotating machinery is widely applied in industrial fields. However, it generally operates
under tough working conditions, which leads to the weak fault features and renders fault …

Wind power has developed rapidly over the past decade where study on wind turbine fault
diagnosis methods are of great significance. The conventional intelligent diagnosis …

In this paper, a novel fault diagnosis framework for rotating machinery is put forward,
centering on a firstly proposed dimensionality reduction algorithm named local-dictionary …