A Case Study of Bearing Fault Monitoring Techniques for Induction Motors

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Authors

  • Ashish Kumar Sinha
     Dept. of Mining Machinery Engineering, Indian School of Mines, Dhanbad 826004 ,IN
  • Sukanta Das
     Dept. of Electrical Engineering, Indian School of Mines, Dhanbad 826004 ,IN
  • Tarun Kumar Chatterjee
     Dept. of Mining Machinery Engineering, Indian School of Mines, Dhanbad 826004 ,IN

Keywords:

Condition Monitoring, Induction Motors, Bearing Faults, Current Signal, Vibration Signal, Fourier Transform, Wavelet Transform.

Abstract

Research has witnessed considerable advancement in the field of fault detection and monitoring in induction motors. The advent of on-line and automated systems for fault diagnosis has added a new dimension in the area of condition monitoring of induction motors. So keeping in mind the vast scope of this field of research and the economic impact that bearing failures have on industries, a review of different techniques used for bearing fault detection is done by compiling various available literatures.In this paper the different bearing fault detection methodologies are grouped according to the techniques used for bearing fault detection. The advantages and disadvantages associated with the fault detection schemes are also reported. The review illustrates that bearing fault detection is primarily done using Fourier transform based analysis and/or Wavelet transform based tools by analyzing vibration signals and/or motor current signals. The vibration signals have proved to be the superior option pertaining to the various advantages as discussed in the literature. The objective of the present work is to unfold a broad area of the updated status of the bearing fault monitoring and will assist future researchers to realize the scope of research in this area at a glimpse.

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Published

2022-10-17

How to Cite

Sinha, A. K., Das, S., & Chatterjee, T. K. (2022). A Case Study of Bearing Fault Monitoring Techniques for Induction Motors. Journal of Mines, Metals and Fuels, 64(5), 249–255. Retrieved from https://informaticsjournals.com/index.php/jmmf/article/view/31527

Issue

Volume 64, Issue 5, May 2016

Section

Articles

License

Creative Commons License

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

 

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