DocumentCode :
3355929
Title :
The advantages of machine fault detection using artificial neural network and fuzzy logic technologies
Author :
Chow, Mo-Yuen
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
fYear :
1994
fDate :
5-9 Dec 1994
Firstpage :
83
Lastpage :
87
Abstract :
Machine fault detection has been attracting significant attention from industry. The early detection of faults in rotating machines can significantly enhance the safety, reliability, and economic issues of industrial operations. With the emerging technology of artificial neural networks and fuzzy logic, the motor fault detection problem can be solved using an innovative approach based on easy accessible measurements, without the need for expensive equipment or accurate mathematical models that are required from conventional fault detection techniques. This paper describes the advantages and the challenge of using the technology of artificial neural networks to solve motor fault detection problems, and also highlights parts of the research results obtained by the author
Keywords :
electric motors; fault diagnosis; fuzzy logic; fuzzy systems; industries; learning (artificial intelligence); neural nets; electric motors; fault diagnosis; fuzzy logic; industrial operations; input-output mapping; learning; machine fault detection; neural network; rotating machines; Artificial neural networks; DC motors; Electrical fault detection; Fault detection; Fuzzy logic; Mathematical model; Power engineering and energy; Power generation economics; Reliability engineering; Safety;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 1994., Proceedings of the IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
0-7803-1978-8
Type :
conf
DOI :
10.1109/ICIT.1994.467184
Filename :
467184
Link To Document :
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