Fault Diagnosis in Electric Generators: Methods, Trends and Challenges

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.

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Publication Details

Journal
Energies
Published
2025-11-27
DOI
https://doi.org/10.3390/en18236210
Citations
1
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
1.21
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article

Fault Diagnosis in Electric Generators: Methods, Trends and Challenges

Epaminondas D. Mitronikas, Konstantinos Koutrakos, Konstantinos Ptochos
1 citations
Energies
Machine Fault Diagnosis Techniques
1.21
article

Fault Diagnosis in Electric Generators: Methods, Trends and Challenges

Epaminondas D. Mitronikas, Konstantinos Koutrakos, Konstantinos Ptochos
article en
1 citations

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.

EnergiesVol. 18(23)
University of Patras (GR)
Openalex Percentile: Top 15%
Machine Fault Diagnosis Techniques
1.21
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