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Thermal imaging fault diagnosis of three-phase induction motors using neural networks.

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Title: Thermal imaging fault diagnosis of three-phase induction motors using neural networks.
Authors: Glowacz, Adam1 (AUTHOR) adglow@agh.edu.pl
Source: Infrared Physics & Technology. May2026, Vol. 155, pN.PAG-N.PAG. 1p.
Subjects: Fault diagnosis, Artificial neural networks, Thermography, Feature extraction, Electric motors, Electric faults, Convolutional neural networks
Abstract: The article presents a technique for diagnosing faults in three-phase induction motors. It uses two thermal imaging cameras and a novel method called Differences of Color Thermal Images (DoCTI). Eight three-phase induction motors (TPIMs) were analyzed: four 550 W motors and four 500 W motors, under the following conditions: healthy, faulty squirrel cage ring, one broken bar, two broken bars, and three broken bars. Thermographic measurements were conducted with thermal camera vibrations ranging from 0 to 1.2 meters per second squared. A novel feature extraction method for color thermal images (DoCTI) was proposed. Three neural networks, NnetV04, NnetV05, and NnetV06, were presented. Convolutional neural networks were used to analyze the thermal images. High accuracy recognition of motor fault conditions was achieved. The computed results confirm the effectiveness of the proposed approach for the recognition of electrical faults of three-phase induction motors. [ABSTRACT FROM AUTHOR]
Copyright of Infrared Physics & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192618001
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Thermal imaging fault diagnosis of three-phase induction motors using neural networks.
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  Data: <searchLink fieldCode="AR" term="%22Glowacz%2C+Adam%22">Glowacz, Adam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adglow@agh.edu.pl</i>
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Thermography%22">Thermography</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+motors%22">Electric motors</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+faults%22">Electric faults</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink>
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  Label: Abstract
  Group: Ab
  Data: The article presents a technique for diagnosing faults in three-phase induction motors. It uses two thermal imaging cameras and a novel method called Differences of Color Thermal Images (DoCTI). Eight three-phase induction motors (TPIMs) were analyzed: four 550 W motors and four 500 W motors, under the following conditions: healthy, faulty squirrel cage ring, one broken bar, two broken bars, and three broken bars. Thermographic measurements were conducted with thermal camera vibrations ranging from 0 to 1.2 meters per second squared. A novel feature extraction method for color thermal images (DoCTI) was proposed. Three neural networks, NnetV04, NnetV05, and NnetV06, were presented. Convolutional neural networks were used to analyze the thermal images. High accuracy recognition of motor fault conditions was achieved. The computed results confirm the effectiveness of the proposed approach for the recognition of electrical faults of three-phase induction motors. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Infrared Physics & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.infrared.2026.106490
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Thermography
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Electric motors
        Type: general
      – SubjectFull: Electric faults
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
    Titles:
      – TitleFull: Thermal imaging fault diagnosis of three-phase induction motors using neural networks.
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            NameFull: Glowacz, Adam
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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            – Type: issn-print
              Value: 13504495
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              Value: 155
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            – TitleFull: Infrared Physics & Technology
              Type: main
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