Fault Prediction of Induction Motor using Machine Learning Algorithm

International Journal of Electrical and Electronics Engineering

© 2021 by SSRG - IJEEE Journal

Volume 8 Issue 11

Year of Publication : 2021

Authors : B. Balaji, Kanagaraj. U, Mahendran. R, Rethinasiranjeevi. R

10.14445/23488379/IJEEE-V8I11P101

How to Cite?

B. Balaji, Kanagaraj. U, Mahendran. R, Rethinasiranjeevi. R, "Fault Prediction of Induction Motor using Machine Learning Algorithm," SSRG International Journal of Electrical and Electronics Engineering, vol. 8,  no. 11, pp. 1-6, 2021. Crossref, https://doi.org/10.14445/23488379/IJEEE-V8I11P101

Abstract:

Induction motor fault identification prior to the occurrence of total shut-down is critical for industries. The identification of faults based on condition monitoring techniques and the use of machine learning has enormous potential. Machine learning's power may be harnessed and properly applied for motor defect detection. To avoid losses, the issue, particularly in induction motors, must be repaired at the appropriate time. Machine learning algorithm applications in the sphere of defect detection give a dependable and effective preventative maintenance solution. In this paper, an algorithm-based machine learning approach is developed to learn features from the frequency distribution of vibration signals with the goal of characterizing the working status of induction motors such as current, voltage, and temperature, and it is also updated in the IoT-based application. It combines feature extraction and classification tasks to enable automated and intelligent problem diagnosis.

Keywords:

Induction Motor, Machine, Fault Prediction

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