International Journal of

Automation and Smart Technology

M. K. M. Rahman1

1Department of EEE, United International University, Madani Avenue, Badda, Dhaka, Bangladesh

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ABSTRACT

Early detection of motor faults can save the motor from subsequent deterioration into more severe conditions, and thus can save lot of maintenance costs. In this work, we have developed automatic motor fault detection system based on artificial neural-network (ANN) using two basic signals: current and sound. Motor condition is captured through current and sound signals which are then preprocessed, and a compressed feature vector is created in their frequency domain. Feature vectors from different motor conditions are used to train a neural network (NN). Once the training is done, the NN-system is ready to monitor motor health condition. Our experimental results show that our NN-based system can successfully detect different motor faults unto 99% accuracy. Both current and sound signals are thoroughly compared under different operating conditions of motors, where both single and three phase motors are considered. The hardware of the system comprised of low-cost CT (current transformer) and microphone that leads to a very cost effective solution. Detailed comparative results are presented that show the suitability of current, sound and their hybrid signal in different scenario. The robustness of the system is evaluated under different conditions such as environmental noise and system’s parameters.


Keywords: Motor Faults, Fault Detection, Current & Sound Analysis, Neural Network


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ARTICLE INFORMATION

Received: 2019-04-07
Revised: 2019-09-28
Accepted: 2021-02-16
Available Online: 2021-07-01


Cite this article:

M. K. M. Rahman. (2021) Motor Fault Detection using Current and Sound: A comparative Study. Int. j. autom. smart technol. https://doi.org/10.5875/ausmt.v11i1.2151

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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