An Efficient Fault Detection Method for Induction Motors Using Thermal Imaging and Machine Vision

Muhammad Rameez Javed, Zain Shabbir, Furqan Asghar, Waseem Amjad, Faisal Mahmood, Muhammad Omer Khan, Umar Siddique Virk, Aashir Waleed and Zunaib Maqsood Haider
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Muhammad Rameez Javed: Department of Electrical, Electronics and Telecommunication Engineering, University of Engineering and Technology, Faisalabad Campus, Lahore 38000, Pakistan
Zain Shabbir: Department of Electrical, Electronics and Telecommunication Engineering, University of Engineering and Technology, Faisalabad Campus, Lahore 38000, Pakistan
Furqan Asghar: Department of Energy Systems Engineering, University of Agriculture, Faisalabad 38000, Pakistan
Waseem Amjad: Department of Energy Systems Engineering, University of Agriculture, Faisalabad 38000, Pakistan
Faisal Mahmood: Department of Energy Systems Engineering, University of Agriculture, Faisalabad 38000, Pakistan
Muhammad Omer Khan: Department of Electrical Engineering & Technology, Riphah International University, Faisalabad 38000, Pakistan
Umar Siddique Virk: Department of Mechatronics and Control Engineering, University of Engineering and Technology, Faisalabad Campus, Lahore 38000, Pakistan
Aashir Waleed: Department of Electrical, Electronics and Telecommunication Engineering, University of Engineering and Technology, Faisalabad Campus, Lahore 38000, Pakistan
Zunaib Maqsood Haider: Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Sustainability, 2022, vol. 14, issue 15, 1-17

Abstract: Induction motors (IMs) are the backbone of industry, and play a vital role in daily life as well. However, induction motors face various faults during their operation, which may cause overheating, energy losses, and failure in the motors. Keeping in mind the severity of the issues associated with fault occurrence, this paper proposes a novel method of fault detection in induction motors by using “Machine Vision (MV)” along with “Infrared Thermography (IRT)”. It is worth mentioning that the timely prevention of faults in the IM ensures the motor’s safety from failures, and provides longer service life. In this work, a dataset of thermal images of an induction motor under different conditions (i.e., normal operation, overloaded, and fault) was developed using an infrared camera without disturbing the working condition of the motor. Then, the extracted thermal images were effectively used for the feature extraction and training by local octa pattern (LOP) and support-vector machine (SVM) classifiers, respectively. In order to enhance the quality of feature extraction from images, the LOP was implemented along with a genetic algorithm (GA). Finally, the proposed methodology was implemented and validated by detecting the faults introduced in an induction motor in real time. In addition to that, a comparative study of the suggested methodology with existing methods also verified the supremacy and effectiveness of the proposed method in comparison to the previous techniques.

Keywords: induction motor; fault detection; local octa pattern; thermal imaging; support-vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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