Online fault diagnosis of large electrical machines using vibration signal-a review

Publikationstyp
Conference Paper
Date Issued
2017-05
Sprache
English
Author(s)
Sadeghi, Iman
Ehya, Hossein
Faiz, Jawad
Ostovar, Hossein  
TORE-URI
https://hdl.handle.net/11420/45788
Citation
International Conference on Optimization of Electrical and Electronic Equipment, OPTIM (2017)
Contribution to Conference
International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 & International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017  
Publisher DOI
10.1109/OPTIM.2017.7975013
Publisher
IEEE
Large electrical machine has been extensively used in power plants and industries. The overhaul for large electrical machine is a costly process; also, unexpected stoppage of product line in factories due to fault leads to huge economical loss. Different methods have been so far proposed for fault diagnosis of large electrical machines. This paper provides comprehensive review of prevalent faults in large electrical machines. These faults include eccentricity fault, rotor broken bar fault and short circuit fault. The impacts of these faults on the vibration signal of machines are surveyed. Analytical method based on air gap magnetic field in presence of different kind of fault is used to extract vibration signal. Finally, distinctive efficient methods for condition monitoring of large electrical machine using vibration signal is introduced and their competency are compared to each other.
DDC Class
621: Applied Physics
620: Engineering