Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault
Martins, J.F.
Pires, V.F.
Pires, A.J.
Escola Superior Tecnologia de Setubal, Instituto Politecnico de Setubal;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 54,
Issue: 1
On page(s): 259-264
ISSN: 0278-0046
INSPEC Accession Number: 9299049
Digital Object Identifier: 10.1109/TIE.2006.888790
Current Version Published: 2007-02-05
Abstract
In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1kW induction motor. The obtained experimental results show the effectiveness of the proposed method
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
