Fault diagnosis of rolling element bearing using Naïve Bayes classifier
Xiao Jian Yi1, Yue Feng Chen2, Peng Hou3
1Department of Overall Technology, China North Vehicle Research Institute, Beijing, China
2The Fourth Research Laboratory, Beijing Special Vehicle Institute, Beijing, China
3School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
E-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Abstract. The development of machine learning brings a new way for diagnosing the fault of rolling element bearings. However, the method in machine learning with high accuracy often has the poor ability of generalization due to the overuse of feature engineering. To address this challenge, Naïve Bayes classifier is applied in this paper. As the one of the cluster of Bayes classifiers, its ability of classification is very outstanding. In this paper, the method is provided with a detailed description for why and how to diagnose the fault of bearing. Finally, an evaluation of the performance of Naïve Bayes classifier is presented with real world data. The evaluation indicates that Naïve Bayes classifier can achieve a high level of accuracy without any feature engineering.
Keywords: Naïve Bayes classifier, machine learning, fault diagnosis, rolling element bearing.
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Cite this article
Yi Xiao Jian, Chen Yue Feng, Hou Peng Fault diagnosis of rolling element bearing using Naïve Bayes classifier. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 64‑69.
© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533