期刊
JOURNAL OF SENSORS
卷 2017, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2017/6737295
关键词
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资金
- National Natural Science Foundation of China [51475097]
- Ministry of Industry and Intelligent Manufacturing Demonstration Project (Ministry of Industry) [[2016] 213]
- Program of Guizhou Province of China [JZ[2014]2001, [2015]02, [2016]5103]
Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens ofmethods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision treemodel. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models.
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