Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detection of Faults in Rotating Machinery Using Periodic Time-Frequency
  Sparsity   This paper addresses the problem of extracting periodic oscillatory features
in vibration sig- nals for detecting faults in rotating machinery. To extract
the feature, we propose an approach in the short-time Fourier transform (STFT)
domain where the periodic oscillatory feature man- ifests itself as a
relatively sparse grid. To estimate the sparse grid, we formulate an
optimization problem using customized binary weights in the regularizer, where
the weights are formulated to promote periodicity. In order to solve the
proposed optimization problem, we develop an algorithm called augmented
Lagrangian majorization-minimization algorithm, which combines the split
augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization
(MM), and is guaranteed to converge for both convex and non-convex formulation.
As examples, the proposed approach is applied to simulated data, and used as a
tool for diagnosing faults in bearings and gearboxes for real data, and
compared to some state-of-the-art methods. The results show the proposed
approach can effectively detect and extract the periodical oscillatory
features.

22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas