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

• Published in: Frontiers of information technology & electronic engineering Vol. 14; no. 12; pp. 941 - 952
• Main Authors: Uysal, Ali, Bayir, Raif
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2013; Springer Nature B.V
• Subjects: Artificial neural networks; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Condition monitoring; Data acquisition; Electric motors; Electrical Engineering; Electronics and Microelectronics; Fault detection; Fault diagnosis; Faults; Graphical user interface; Instrumentation; Kohonen神经网络; Matlab; Microprocessors; Networks; Neural networks; Position measurement; Real time; SRM; User interface; 人工神经网络; 图形用户界面; 实时状态监测; 开关磁阻电机; 故障诊断; Switched reluctance motor; TP183; Kohonen neural network; Real-time condition monitoring; Fault detection and diagnosis; TM32
• ISSN: 1869-1951; 2095-9184; 1869-196X; 2095-9230
• 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 de- tecting 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.