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Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning.

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Title: Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning.
Authors: Glowacz, Adam1 (AUTHOR) adglow@agh.edu.pl, Sulowicz, Maciej2 (AUTHOR) maciej.sulowicz@pk.edu.pl, Zielonka, Jakub2 (AUTHOR) jakub.zielonka@doktorant.pk.edu.pl, Li, Zhixiong3 (AUTHOR) z.li@po.edu.pl, Glowacz, Witold1 (AUTHOR) wglowacz@agh.edu.pl, Kumar, Anil4 (AUTHOR) 20210129@wzu.edu.cn
Source: Expert Systems with Applications. Mar2025, Vol. 262, pN.PAG-N.PAG. 1p.
Subjects: Fault diagnosis, Production losses, Deep learning, Feature extraction, Factories, Induction motors
Abstract: Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1–1225 Hz and word coding. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 181497165
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning.
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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><br /><searchLink fieldCode="AR" term="%22Sulowicz%2C+Maciej%22">Sulowicz, Maciej</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> maciej.sulowicz@pk.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Zielonka%2C+Jakub%22">Zielonka, Jakub</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jakub.zielonka@doktorant.pk.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhixiong%22">Li, Zhixiong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> z.li@po.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Glowacz%2C+Witold%22">Glowacz, Witold</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wglowacz@agh.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Anil%22">Kumar, Anil</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> 20210129@wzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Mar2025, Vol. 262, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Production+losses%22">Production losses</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Factories%22">Factories</searchLink><br /><searchLink fieldCode="DE" term="%22Induction+motors%22">Induction motors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1–1225 Hz and word coding. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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:
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      – Type: doi
        Value: 10.1016/j.eswa.2024.125633
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Production losses
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Factories
        Type: general
      – SubjectFull: Induction motors
        Type: general
    Titles:
      – TitleFull: Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning.
        Type: main
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            NameFull: Glowacz, Adam
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            NameFull: Sulowicz, Maciej
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            NameFull: Zielonka, Jakub
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            NameFull: Li, Zhixiong
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            NameFull: Glowacz, Witold
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            NameFull: Kumar, Anil
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          Dates:
            – D: 01
              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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              Value: 09574174
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              Value: 262
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            – TitleFull: Expert Systems with Applications
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