Fault Detection of Electrical Machines Rolling Element and Prediction through AI

Authors

  • Mureed Hussain Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan
  • Ali Suqrat Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan
  • Muhammad Shaheer Amir Bajwa Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan
  • Kainat Fatima Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan

DOI:

https://doi.org/10.57041/vol2iss2pp10-15

Keywords:

Rolling element fault, ANN, Kurtosis, MATLAB, Dataset

Abstract

The research examines how Artificial Intelligence (AI) and machine learning can identify a minor rolling element problem (hole and scratch). Keeping track of a machine's health makes it possible to spot problems before they become costly, unscheduled production process shutdowns. In the study, a multi-stage decision mechanism was built using the convolution neural network (CNN) model.

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Published

2022-12-30

Issue

Vol. 2 No. 2 (2022): Pakistan Journal of Scientific Research

Section

Articles

License

Copyright (c) 2022 https://paas-pk.org/index.php/pjosr/cr

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite

Fault Detection of Electrical Machines Rolling Element and Prediction through AI. (2022). Pakistan Journal of Scientific Research, 2(2), 10-15. https://doi.org/10.57041/vol2iss2pp10-15

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