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Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis


[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication


Published in

Eksploatacja i Niezawodność – Maintenance and Reliability

Journal year: 2023 | Journal volume: vol. 25 | Journal number: no. 3

Article type

scientific article

Publication language


  • thermal imaging
  • fault diagnosis
  • convolutional neural networks
  • explainability
  • squirrel-cage induction motor
  • edge processing

EN 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.

Pages (from - to)

1 - 16




License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Release date


Date of Open Access to the publication

at the time of publication

Full text of article

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Ministry points / journal


Impact Factor

2.5 [List 2022]

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