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CLC number: TM32; TP183

On-line Access: 2013-12-06

Received: 2013-04-09

Revision Accepted: 2013-10-08

Crosschecked: 2013-11-18

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.12 P.941-952


Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

Author(s):  Ali Uysal, Raif Bayir

Affiliation(s):  Department of Mechatronics Engineering, Faculty of Technology, Karabuk University, Karabük 78050, Turkey

Corresponding email(s):   rbayir@karabuk.edu.tr

Key Words:  Switched reluctance motor, Kohonen neural network, Real-time condition monitoring, Fault detection and diagnosis

Ali Uysal, Raif Bayir. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. Journal of Zhejiang University Science C, 2013, 14(12): 941-952.

@article{title="Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network",
author="Ali Uysal, Raif Bayir",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
%A Ali Uysal
%A Raif Bayir
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 12
%P 941-952
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300085

T1 - Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
A1 - Ali Uysal
A1 - Raif Bayir
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 12
SP - 941
EP - 952
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300085

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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