DocumentCode :
3508852
Title :
An on-line monitoring and diagnostic method of rolling element bearing with AI
Author :
Shao, Yimin ; Nezu, Kiktto
Author_Institution :
Dept. of Mech. Eng., Gunma Univ., Japan
fYear :
1995
fDate :
26-28 Jul 1995
Firstpage :
1543
Lastpage :
1548
Abstract :
A new concept of the degree of creditability of parameter value variations (DCPV factor) is proposed in this paper to solve problem that on-line monitoring and failure diagnosis of rolling element bearings are affected by monitoring parameter value variations caused by the intrusive vibration signals. Using the factor of the degree of creditability and the basic principle of expert systems, an on-line monitoring and diagnostic method of rolling element bearings with AI is developed. The technique enhances traditional vibration analysis and provides a means of automating the monitoring and diagnosis of a vibrating device
Keywords :
computerised monitoring; diagnostic expert systems; fuzzy logic; mechanical engineering; monitoring; degree of creditability; diagnostic method; expert systems; intrusive vibration signals; online monitoring; rolling element bearing; vibrating device; Accelerometers; Artificial intelligence; Computerized monitoring; Condition monitoring; Diagnostic expert systems; Pulleys; Rolling bearings; Surface cracks; Testing; Vibrations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SICE '95. Proceedings of the 34th SICE Annual Conference. International Session Papers
Conference_Location :
Hokkaido
Print_ISBN :
0-7803-2781-0
Type :
conf
DOI :
10.1109/SICE.1995.526964
Filename :
526964
Link To Document :
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