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 approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

Total variation denoising is a nonlinear filtering method well suited for the estimation of
piecewise-constant signals observed in additive white Gaussian noise. The method is …

Bearing fault diagnosis is critical for rotating machinery condition monitoring since it is a key
component of rotating machines. One of the challenges for bearing fault diagnosis is to …

Vibration signals arising from faulty gearboxes are often a mixture of the meshing
component and the periodic transient component, and simultaneously contaminated by …

Planetary subassemblies in wind turbine gearbox are subject to compound faults due to
harsh environment and complex structure. Disturbed by the meshing vibration from higher …

Extraction of fault-induced transients is one of the difficulties in gear fault diagnosis as the
vibration signal is composed of multiple components. Sparse representation is one of the …

The calculation of a sparse approximate solution to a linear system of equations is often
performed using either L1-norm regularization and convex optimization or nonconvex …

Many real-world problems involve the recovery of a matrix from linear measurements, where
the matrix lies close to some low-dimensional structure. This paper considers the problem of …

Early detection of faults developed in gearboxes is of great importance to prevent
catastrophic accidents. In this paper, a sparsity-based feature extraction method using the …