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Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions.

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Title: Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions.
Authors: Aimer, Ameur Fethi1,2 fethi.aimer@yahoo.fr, Boudinar, Ahmed Hamida2,3 boud_ah@yahoo.fr, Khodja, Mohamed El-Amine2,3 koudjamea@gmail.com, Bendiabdellah, Azeddine2,3 bendiazz@yahoo.fr
Source: Telkomnika. Apr2026, Vol. 24 Issue 2, p717-726. 10p.
Subjects: Fault diagnosis, Autoregressive models, Signal processing, Induction motors, Transient analysis
Abstract: In this paper, an auto regressive (AR) model-based approach is applied in the stator current analysis under non-stationary conditions (case of frequency variation due to variable speed operation). Under these conditions, the identification of fault signatures is almost impossible due the variation of the fundamental frequency using conventional analysis methods. Moreover, this approach is used in the diagnosis of multiple faults occurring simultaneously in induction motor drives. In this aim, the stator current signal is decomposed into short segments then the AR modeling approach is applied on each segment. This approach called short-time ROOT-AR is then applied to solve the problem of the non-stationarity of the stator current signal under variable speed operation. The efficiency of the short-time ROOT-AR approach is evaluated through experimental tests in the diagnosis of multiple faults occurring simultaneously in induction motor drive. Finally, the superiority of the proposed approach is highlighted in comparison with conventional techniques in terms of accuracy, computational time and robustness against the noise. [ABSTRACT FROM AUTHOR]
Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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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Header DbId: egs
DbLabel: Engineering Source
An: 194026223
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PubTypeId: academicJournal
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  Data: Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions.
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  Data: <searchLink fieldCode="AR" term="%22Aimer%2C+Ameur+Fethi%22">Aimer, Ameur Fethi</searchLink><relatesTo>1,2</relatesTo><i> fethi.aimer@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Boudinar%2C+Ahmed+Hamida%22">Boudinar, Ahmed Hamida</searchLink><relatesTo>2,3</relatesTo><i> boud_ah@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Khodja%2C+Mohamed+El-Amine%22">Khodja, Mohamed El-Amine</searchLink><relatesTo>2,3</relatesTo><i> koudjamea@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Bendiabdellah%2C+Azeddine%22">Bendiabdellah, Azeddine</searchLink><relatesTo>2,3</relatesTo><i> bendiazz@yahoo.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Apr2026, Vol. 24 Issue 2, p717-726. 10p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Autoregressive+models%22">Autoregressive models</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Induction+motors%22">Induction motors</searchLink><br /><searchLink fieldCode="DE" term="%22Transient+analysis%22">Transient analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, an auto regressive (AR) model-based approach is applied in the stator current analysis under non-stationary conditions (case of frequency variation due to variable speed operation). Under these conditions, the identification of fault signatures is almost impossible due the variation of the fundamental frequency using conventional analysis methods. Moreover, this approach is used in the diagnosis of multiple faults occurring simultaneously in induction motor drives. In this aim, the stator current signal is decomposed into short segments then the AR modeling approach is applied on each segment. This approach called short-time ROOT-AR is then applied to solve the problem of the non-stationarity of the stator current signal under variable speed operation. The efficiency of the short-time ROOT-AR approach is evaluated through experimental tests in the diagnosis of multiple faults occurring simultaneously in induction motor drive. Finally, the superiority of the proposed approach is highlighted in comparison with conventional techniques in terms of accuracy, computational time and robustness against the noise. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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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    Identifiers:
      – Type: doi
        Value: 10.12928/TELKOMNIKA.v24i2.27624
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 10
        StartPage: 717
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Autoregressive models
        Type: general
      – SubjectFull: Signal processing
        Type: general
      – SubjectFull: Induction motors
        Type: general
      – SubjectFull: Transient analysis
        Type: general
    Titles:
      – TitleFull: Simultaneous faults diagnosis and prognostic in induction motor drives under nonstationary conditions.
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            NameFull: Aimer, Ameur Fethi
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            NameFull: Boudinar, Ahmed Hamida
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            NameFull: Khodja, Mohamed El-Amine
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            NameFull: Bendiabdellah, Azeddine
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          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 24
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