Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bearings Fault Detection Using Hidden Markov Models and Principal
  Component Analysis Enhanced Features   Asset health monitoring continues to be of increasing importance on
productivity, reliability, and cost reduction. Early Fault detection is a
keystone of health management as part of the emerging Prognostics and Health
Management (PHM) philosophy. This paper proposes a Hidden Markov Model (HMM) to
assess the machine health degradation. using Principal Component Analysis (PCA)
to enhance features extracted from vibration signals is considered. The
enhanced features capture the second order structure of the data. The
experimental results based on a bearing test bed show the plausibility of the
proposed method.

4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn