Fault Diagnosis in Electric Generators: Methods, Trends and Challenges.
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| Title: | Fault Diagnosis in Electric Generators: Methods, Trends and Challenges. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en18236210 |
