Fault diagnosis of rolling element bearing using Naïve Bayes classifier

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.