Condition monitoring and fault diagnosis of serial wound starter motor with learning vector quantization network

被引：7
作者：
Bayir, R. [1 ]
机构：
[1] Department of Electronic and Computer, Faculty of Technical Education, Karabuk University, 78050, Karabuk
来源：
Journal of Applied Sciences | 2008年 / 8卷 / 18期
关键词：
Condition monitoring; Fault diagnosis; Learning vector quantization neural network; Starter motor;
D O I：
10.3923/jas.2008.3148.3156
中图分类号：
学科分类号：
摘要：
In this study, a Graphical User Interface (GUI) software for real time condition monitoring and fault diagnosis of serial wound starter motors has been developed using Learning Vector Quantization (LVQ) neural network. The starter motors are serial wound dc motors which enable the Internal Combustion Engine (ICE) to run. When the starter motor fault occurs, the ICE cannot be run. Therefore, condition monitoring and pre-diagnosis of starter motor faults are important. The information of voltages and currents is acquired from the starter motor via data acquisition card and transferred to the program. With this program using LVQ network, six faults observed in the starter motors were successfully detected and diagnosed in real time. The GUI software makes it possible to condition monitoring and diagnose the faults in starter motors before they occur by keeping fault records of the past occurrences. This system can be used in service shops and in test departments of starter motor manufacturers. In addition, this system has potential to be used for real time condition monitoring and fault diagnosis of vehicles with the help of industrial computers. © 2008 Asian Network for Scientific Information.
引用
收藏
页码：3148 / 3156
页数：8
相关论文
共 27 条
  • [1] Abu-Mabfouz I.A., A comparative study of three artificial neural networks for the detection and classification of gear faults, Int. J. General Syst., 34, pp. 261-277, (2005)
  • [2] Bassiuny A.M., Xiaoli L., Du R., Fault diagnosis of stamping process based on empirical mode decomposition and learning vector quantization, Int. J. Mach. Tools Manuf., 47, pp. 2298-2306, (2007)
  • [3] Bay O.F., Bayir R., Kohonen network based fault diagnosis and condition monitoring of pre-engaged starter motor, Int. J. Automotive Tech., 6, pp. 341-350, (2005)
  • [4] Bayir R., Bay O.F., Serial wound starter motor faults diagnosis using artificial neural network, pp. 194-199, (2004)
  • [5] Bolera K., Automotive Electric/Electronic Systems, pp. 346-375, (1995)
  • [6] Chow M.Y., Sharpe R.N., Hung J., On the application and design of artificial neural networks for motor fault detection-Part I, IEEE Trans. Ind. Elect., 40, pp. 181-188, (1993)
  • [7] Chow M.Y., Sharpe R.N., Hung J., On the application and design of artificial neural networks for motor fault detection-Part II, IEEE Trans. Ind. Elect., 40, pp. 189-196, (1993)
  • [8] Chow M.Y., Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection, (1997)
  • [9] Denton T., Automobile Electrical and Electronic Systems, (2004)
  • [10] Denton T., Advanced Automotive Fault Diagnosis, (2006)
123