Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …

Sparse priors for signals play a key role in sparse signal modeling, and sparsity-assisted
signal processing techniques have been studied widely for machinery fault diagnosis. In this …

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 …

Weak machinery fault detection is a difficult task because of two main reasons (1) At the
early stage of fault development, signature of fault related component performs incompletely …

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 …

Bearing fault is the most common causes of rotating machinery failure. Therefore, accurate
bearing fault identification technique is of tremendous significance. Vibration monitoring has …

Acoustic signals have attracted considerable attention in mechanical fault diagnosis
because of their advantages in non-invasive technique, instant measurement and low cost …

Sparse models are particularly useful in scientific applications, such as biomarker discovery
in genetic or neuroimaging data, where the interpretability of a predictive model is essential …

The vibration signal of faulty rotating machinery tends to be a mixture of repetitive transients,
discrete frequency components and noise. How to accurately extract the repetitive transients …

Robust sparse representation has shown significant potential in solving challenging
problems in computer vision such as biometrics and visual surveillance. Although several …