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Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis
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Institute of Robotics and Machine Intelligence, Poznań University of Technology, Poland
Department of Electrical Machines Drives and Measurements, Wroclaw University of Science and Technology, Poland
Submission date: 2023-06-29
Final revision date: 2023-07-19
Acceptance date: 2023-07-27
Online publication date: 2023-07-29
Publication date: 2023-07-29
Corresponding author
Mateusz Piechocki   

Institute of Robotics and Machine Intelligence, Poznań University of Technology, Poznań, Poland
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(3):170114
  • Fault diagnosis is necessary to ensure the electromechanical system's reliability.
  • Thermal imaging can be utilized to diagnose the induction motor state.
  • Explainability methods provide insights into decision-making process of neural algorithms.
  • Convolutional neural networks can efficiently operate on resource-constrained hardware.
  • There is no universal approach for deploying deep learning algorithms on edge devices.
Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.
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