Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning and Deep Learning Algorithms for Bearing Fault
  Diagnostics -- A Comprehensive Review   In this survey paper, we systematically summarize existing literature on
bearing fault diagnostics with machine learning (ML) and data mining
techniques. While conventional ML methods, including artificial neural network
(ANN), principal component analysis (PCA), support vector machines (SVM), etc.,
have been successfully applied to the detection and categorization of bearing
faults for decades, recent developments in deep learning (DL) algorithms in the
last five years have sparked renewed interest in both industry and academia for
intelligent machine health monitoring. In this paper, we first provide a brief
review of conventional ML methods, before taking a deep dive into the
state-of-the-art DL algorithms for bearing fault applications. Specifically,
the superiority of DL based methods over conventional ML methods are analyzed
in terms of fault feature extraction and classification performances; many new
functionalities enabled by DL techniques are also summarized. In addition, to
obtain a more intuitive insight, a comparative study is conducted on the
classification accuracy of different algorithms utilizing the open-source Case
Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the
transition on applying various DL algorithms to bearing fault diagnostics,
detailed recommendations and suggestions are provided for specific application
conditions such as the setup environment, the data size, and the number of
sensors and sensor types. Future research directions to further enhance the
performance of DL algorithms on health monitoring are also discussed.

32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat