Journal
SENSORS
Volume 18, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/s18020463
Keywords
Naive Bayes; decision tree; support vector machines; fault diagnosis
Funding
- National Natural Science Foundation of China [61702348, 61772351, 61602326]
- National Key Technology Research and Development Program [2015BAF13B01]
- National Key RD Plan [2017YFB1303000, 2017YFB1302800]
- Beijing Municipal Science & Technology Commission [LJ201607]
- Youth Innovative Research Team of Capital Normal University
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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.
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