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

Ali UYSAL; Raif BAYIR

doi:10.1631/jzus.C1300085

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

电气工程 | Updated：2022-12-09

- Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
- Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network
- 信息与电子工程前沿（英文） 2013年14卷第12期页码：941-952
- Affiliations：
  
  Department of Mechatronics Engineering, Faculty of Technology, Karabuk University, Karabük 78050, Turkey
- Author bio：
  
  Raif BAYIR, E-mail: rbayir@karabuk.edu.tr
- Funds：
  
  Project (No. KBü-BAP-C-11-D-003) supported by the Karabük University BAP Unit, Turkey
- DOI：10.1631/jzus.C1300085
  中图分类号： TM32;TP183
- 收稿：2013-04-09，
  
  修回：2013-11-18，
  
  纸质出版：2013-12
- Accepted：
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Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. 信息与电子工程前沿（英文）, 2013,14(12):941-952.

Ali UYSAL, Raif BAYIR. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(12): 941-952.
Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. 信息与电子工程前沿（英文）, 2013,14(12):941-952. DOI： 10.1631/jzus.C1300085.

Ali UYSAL, Raif BAYIR. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(12): 941-952. DOI： 10.1631/jzus.C1300085.

摘要

Abstract

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.

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references

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