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Fault Diagnosis in Electric Generators: Methods, Trends and Challenges.

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Title: Fault Diagnosis in Electric Generators: Methods, Trends and Challenges.
Authors: Ptochos, Konstantinos1 (AUTHOR), Koutrakos, Konstantinos1 (AUTHOR), Mitronikas, Epameinondas1 (AUTHOR) e.mitronikas@upatras.gr
Source: Energies (19961073). Dec2025, Vol. 18 Issue 23, p6210. 34p.
Subjects: Fault diagnosis, Electric generators, Signal processing, Detection algorithms, Repair & maintenance services, Machine learning, Technological innovations
Abstract: It has been more than a century since the day the first commercial generator was put into operation. Since then, our technical civilization has been dependent on the reliability of electric generators for electrical supply. The reliable and uninterruptible operation of power generators depends heavily on a proper maintenance strategy, and faults occurring during operation should be detected in a timely manner. In this work, a review of state-of-the-art fault diagnosis strategies is presented. Faults occurring in electric generators are presented and categorized, and the quantities utilized for their detection are provided. Traditional signal processing methods and machine learning (ML) approaches for their reliable detection are analyzed. Trends and challenges are discussed, and future directions are highlighted. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI 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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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Dec2025, Vol. 18 Issue 23, p6210. 34p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+generators%22">Electric generators</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Repair+%26+maintenance+services%22">Repair & maintenance services</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink>
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  Data: It has been more than a century since the day the first commercial generator was put into operation. Since then, our technical civilization has been dependent on the reliability of electric generators for electrical supply. The reliable and uninterruptible operation of power generators depends heavily on a proper maintenance strategy, and faults occurring during operation should be detected in a timely manner. In this work, a review of state-of-the-art fault diagnosis strategies is presented. Faults occurring in electric generators are presented and categorized, and the quantities utilized for their detection are provided. Traditional signal processing methods and machine learning (ML) approaches for their reliable detection are analyzed. Trends and challenges are discussed, and future directions are highlighted. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Energies (19961073) is the property of MDPI 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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        Text: English
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      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Electric generators
        Type: general
      – SubjectFull: Signal processing
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      – SubjectFull: Detection algorithms
        Type: general
      – SubjectFull: Repair & maintenance services
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      – SubjectFull: Machine learning
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      – SubjectFull: Technological innovations
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      – TitleFull: Fault Diagnosis in Electric Generators: Methods, Trends and Challenges.
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            – D: 01
              M: 12
              Text: Dec2025
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              Y: 2025
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