Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A multi-stage semi-supervised improved deep embedded clustering method
  for bearing fault diagnosis under the situation of insufficient labeled
  samples   Although data-driven fault diagnosis methods have been widely applied,
massive labeled data are required for model training. However, a difficulty of
implementing this in real industries hinders the application of these methods.
Hence, an effective diagnostic approach that can work well in such situation is
urgently needed.In this study, a multi-stage semi-supervised improved deep
embedded clustering (MS-SSIDEC) method, which combines semi-supervised learning
with improved deep embedded clustering (IDEC), is proposed to jointly explore
scarce labeled data and massive unlabeled data. In the first stage, a
skip-connection-based convolutional auto-encoder (SCCAE) that can automatically
map the unlabeled data into a low-dimensional feature space is proposed and
pre-trained to be a fault feature extractor. In the second stage, a
semi-supervised improved deep embedded clustering (SSIDEC) network is proposed
for clustering. It is first initialized with available labeled data and then
used to simultaneously optimize the clustering label assignment and make the
feature space to be more clustering-friendly. To tackle the phenomenon of
overfitting, virtual adversarial training (VAT) is introduced as a
regularization term in this stage. In the third stage, pseudo labels are
obtained by the high-quality results of SSIDEC. The labeled dataset can be
augmented by these pseudo-labeled data and then leveraged to train a bearing
fault diagnosis model. Two public datasets of vibration data from rolling
bearings are used to evaluate the performance of the proposed method.
Experimental results indicate that the proposed method achieves a promising
performance in both semi-supervised and unsupervised fault diagnosis tasks.
This method provides a new approach for fault diagnosis under the situation of
limited labeled samples by effectively exploring unsupervised data.

36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat