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