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DOI QR Code

Induction Machine Fault Detection Using Generalized Feed Forward Neural Network

  • Ghate, V.N. (Dept. of Electrical Engineering, Govt. College of Engineering Amravati Maharashtra) ;
  • Dudul, S.V. (Dept. of Applied Electronic Engineering, Sant Gadge Baba. Amravati University)
  • 발행 : 2009.09.01

초록

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

키워드

참고문헌

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피인용 문헌

  1. A Fault Severity Index for Stator Winding Faults Detection in Vector Controlled PM Synchronous Motor vol.10, pp.6, 2015, https://doi.org/10.5370/JEET.2015.10.6.2326