The Design of Bearing Fault Diagnosis Classifier Based on the Bayesian Classification

DOI

10.2991/icmeit-16.2016.31How to use a DOI?

Keywords

PCA, Bayesian, Fault Detection

Abstract

The advantages of Principal Component Analysis (PCA) in dimension reduction are obvious, while the classifying methods are various. PCA, applied to Bayesian classification with the minimium risk, is adopted to realize classifying and testing the learning samples in this paper, eventually accurately realizing the classifier training towards the new input sample and then detecting the testing effect.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunhe Shi
AU  - Xiaowei Han
AU  - Zhen Li
PY  - 2016/08
DA  - 2016/08
TI  - The Design of Bearing Fault Diagnosis Classifier Based on the Bayesian Classification
BT  - Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology
PB  - Atlantis Press
SP  - 159
EP  - 162
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeit-16.2016.31
DO  - 10.2991/icmeit-16.2016.31
ID  - Shi2016/08
ER  -