DocumentCode :
3344338
Title :
Study of remote bearing fault diagnosis based on BP Neural Network combination
Author :
Yunyun Yang ; Wei Tang
Author_Institution :
Electr. & Electron. Eng. Inst., Shaanxi Univ. of Sci. & Technol., Xi´an, China
Volume :
2
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
618
Lastpage :
621
Abstract :
In this paper, the rolling bearing is detected by the PLC-based remote fault diagnosis system. This system carries out remote system bearing fault diagnosis and maintenance through the combination of expert system and BP Neural Network. Since the advantages of rapid exchange of diagnostic information, accelerating the fault diagnosis rate, and reducing negative effects of failure on production process, many enterprises adopt remote fault diagnosis technology to carry out rolling bearing detection. Enterprises adopting cross-boundary diagnosis of rolling bearing fault detection show that this method brings enormous economic benefits. The research has important economic significance and practical value.
Keywords :
backpropagation; control engineering computing; expert systems; fault diagnosis; maintenance engineering; mechanical engineering computing; programmable controllers; rolling bearings; BP neural network combination; PLC-based remote fault diagnosis system; cross-boundary diagnosis; diagnostic information; economic benefits; expert system; maintenance; remote bearing fault diagnosis; remote system bearing fault diagnosis; rolling bearing fault detection; Fault detection; Fault diagnosis; Indexes; MATLAB; Neurons; Rolling bearings; Training; BP Neural Network; PLC; remote fault diagnosis; rolling bearing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
ISSN :
2157-9555
Print_ISBN :
978-1-4244-9950-2
Type :
conf
DOI :
10.1109/ICNC.2011.6022177
Filename :
6022177
Link To Document :
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