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