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Open AccessArticle

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

1,2,3,4, 1,2,3,4,*, 1,2,3,4 and 1,2,3,4
College of Information Engineering, Capital Normal University, Beijing 100048, China
Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China
Beijing Key Laboratory of Light Industrial Robot and Safety Verification, Capital Normal University, Beijing 100048, China
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 463;
Received: 21 December 2017 / Revised: 25 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
PDF [1043 KB, uploaded 13 February 2018]


The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. View Full-Text
Keywords: Naive Bayes; decision tree; support vector machines; fault diagnosis Naive Bayes; decision tree; support vector machines; fault diagnosis

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Zhang, N.; Wu, L.; Yang, J.; Guan, Y. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data. Sensors 2018, 18, 463.

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