Grey Wolf Optimized PNN for the Detection of Faults in Induction Motors

Authors

  • N. Sivaraj Research Scholar, Department of Electrical Engineering, Annamalai University, Chidambaram, Tamil Nadu, India
  • B. Rajagopal Associate Professor, Department of Electrical Engineering, Annamalai University, Chidambaram, Tamil Nadu India

Keywords:

Induction Motor, Grey Wolf Optimized PNN, Wiener Filter, Grey-Level Co-Occurrence Matrix

Abstract

Induction motors, commonly referred as asynchronous machines, are the most frequently used electrical machinery in industries. The induction motor (IM) plays a crucial role in many industrial applications in the current world because of its many advantages, including its low cost, sturdy nature, etc. There are a variety of causes for induction motor defects, including overcurrent, undercurrents, starter problems, overvoltage, overloading, motor overheating, etc. Therefore, it is crucial to defend the motor from errors. The objective of this study is to use Grey Wolf Optimized PNN is used to find induction motor defects. In this paper some of the processing operations like Filtering, segmenting, extracting and classifying were carried out on the driven motor's retrieved output signals. The proposed method combines the wiener filter for pre-processing, the Gabor wavelet transform for segmentation, the Grey-Level Co-Occurrence Matrix for extracting features and the Grey Wolf Optimized PNN for classification.

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References

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Proposed work

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Published

16.12.2022

How to Cite

Sivaraj, N. ., & Rajagopal, B. . (2022). Grey Wolf Optimized PNN for the Detection of Faults in Induction Motors. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 16–22. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2191

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Section

Research Article