Advanced Diagnostic Techniques for Earthing Brush Faults Detection in Large Turbine Generators.
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| Title: | Advanced Diagnostic Techniques for Earthing Brush Faults Detection in Large Turbine Generators. |
|---|---|
| Authors: | Mailula, Katudi Oupa1 (AUTHOR), Saha, Akshay Kumar1 (AUTHOR) saha@ukzn.ac.za |
| Source: | Energies (19961073). Jul2025, Vol. 18 Issue 14, p3597. 23p. |
| Subjects: | Fault diagnosis, Condition-based maintenance, Voltage, Electric current grounding, Diagnostic examinations, Electric measurements, Turbine generators, Electric currents |
| Abstract: | Large steam turbine generators are increasingly vulnerable to damage from shaft voltages and bearing currents due to the widespread adoption of modern power electronic excitation systems and more flexible operating regimes. Earthing brushes provide a critical path for discharging these shaft currents and voltages, but their effectiveness depends on the timely detection of brush degradation or faults. Conventional monitoring of shaft voltage and current is often rudimentary, typically limited to peak readings, making it challenging to identify specific fault conditions before mechanical damage occurs. This study addresses this gap by systematically analyzing shaft voltage and current signals under various controlled earthing brush fault conditions (floating brushes, worn brushes, and oil/dust contamination) in several large turbine generators. Experimental site tests identified distinct electrical signatures associated with each fault type, demonstrating that online shaft voltage and current measurements can reliably detect and classify earthing brush faults. These include unique RMS, DC, and harmonic patterns in both voltage and current signals, enabling accurate fault classification. These findings highlight the potential for more proactive maintenance and condition-based monitoring, which can reduce unplanned outages and improve the reliability and safety of power generation systems. [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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| Header | DbId: egs DbLabel: Engineering Source An: 186930343 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advanced Diagnostic Techniques for Earthing Brush Faults Detection in Large Turbine Generators. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mailula%2C+Katudi+Oupa%22">Mailula, Katudi Oupa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saha%2C+Akshay+Kumar%22">Saha, Akshay Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> saha@ukzn.ac.za</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jul2025, Vol. 18 Issue 14, p3597. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Voltage%22">Voltage</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+current+grounding%22">Electric current grounding</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+examinations%22">Diagnostic examinations</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+measurements%22">Electric measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Turbine+generators%22">Turbine generators</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+currents%22">Electric currents</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large steam turbine generators are increasingly vulnerable to damage from shaft voltages and bearing currents due to the widespread adoption of modern power electronic excitation systems and more flexible operating regimes. Earthing brushes provide a critical path for discharging these shaft currents and voltages, but their effectiveness depends on the timely detection of brush degradation or faults. Conventional monitoring of shaft voltage and current is often rudimentary, typically limited to peak readings, making it challenging to identify specific fault conditions before mechanical damage occurs. This study addresses this gap by systematically analyzing shaft voltage and current signals under various controlled earthing brush fault conditions (floating brushes, worn brushes, and oil/dust contamination) in several large turbine generators. Experimental site tests identified distinct electrical signatures associated with each fault type, demonstrating that online shaft voltage and current measurements can reliably detect and classify earthing brush faults. These include unique RMS, DC, and harmonic patterns in both voltage and current signals, enabling accurate fault classification. These findings highlight the potential for more proactive maintenance and condition-based monitoring, which can reduce unplanned outages and improve the reliability and safety of power generation systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18143597 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 3597 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Condition-based maintenance Type: general – SubjectFull: Voltage Type: general – SubjectFull: Electric current grounding Type: general – SubjectFull: Diagnostic examinations Type: general – SubjectFull: Electric measurements Type: general – SubjectFull: Turbine generators Type: general – SubjectFull: Electric currents Type: general Titles: – TitleFull: Advanced Diagnostic Techniques for Earthing Brush Faults Detection in Large Turbine Generators. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mailula, Katudi Oupa – PersonEntity: Name: NameFull: Saha, Akshay Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 14 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
