DocumentCode :
2260668
Title :
The advantages and challenges of machine fault detection using artificial neural network and fuzzy logic technologies
Author :
Chow, Mo-Yuen ; Goode, Paul V.
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
fYear :
1993
fDate :
16-18 Aug 1993
Firstpage :
708
Abstract :
Machine fault detection has always been an important topic in power engineering. The early detection of faults in rotating machines can significantly enhance the safety, reliability, and economic issues of power systems. With the emerging technology of artificial neural networks and fuzzy logic, the motor fault detection problem cam be easily 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 the research results of the National Science Foundation sponsored project Motor Fault Detection Using Artificial Neural Networks
Keywords :
electric machine analysis computing; fault location; fuzzy logic; machine testing; neural nets; artificial neural network; fuzzy logic; machine fault detection; motor faults; power engineering; rotating machines; Artificial neural networks; Electrical fault detection; Fault detection; Power engineering; Power generation economics; Power system economics; Power system faults; Power system reliability; Rotating machines; Safety;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-1760-2
Type :
conf
DOI :
10.1109/MWSCAS.1993.342948
Filename :
342948
Link To Document :
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