Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data
Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan ext...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Charrak, Naas [verfasserIn] Djeddi, Ahmed Zohair [verfasserIn] Hafaifa, Ahmed [verfasserIn] Elbar, Mohammed [verfasserIn] Iratni, Abdelhamid [verfasserIn] Colak, Ilhami [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Life cycle reliability and safety engineering - Springer Nature Singapore, 2017, 13(2024), 3 vom: 12. Juli, Seite 255-275 |
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| Übergeordnetes Werk: |
volume:13 ; year:2024 ; number:3 ; day:12 ; month:07 ; pages:255-275 |
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| DOI / URN: |
10.1007/s41872-024-00259-5 |
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| Katalog-ID: |
SPR057172838 |
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| 520 | |a Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. | ||
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| 700 | 1 | |a Elbar, Mohammed |e verfasserin |4 aut | |
| 700 | 1 | |a Iratni, Abdelhamid |e verfasserin |4 aut | |
| 700 | 1 | |a Colak, Ilhami |e verfasserin |4 aut | |
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10.1007/s41872-024-00259-5 doi (DE-627)SPR057172838 (SPR)s41872-024-00259-5-e DE-627 ger DE-627 rakwb eng 600 VZ 600 VZ Charrak, Naas verfasserin aut Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 Djeddi, Ahmed Zohair verfasserin aut Hafaifa, Ahmed verfasserin (orcid)0000-0002-7812-7429 aut Elbar, Mohammed verfasserin aut Iratni, Abdelhamid verfasserin aut Colak, Ilhami verfasserin aut Enthalten in Life cycle reliability and safety engineering Springer Nature Singapore, 2017 13(2024), 3 vom: 12. Juli, Seite 255-275 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:13 year:2024 number:3 day:12 month:07 pages:255-275 https://dx.doi.org/10.1007/s41872-024-00259-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2024 3 12 07 255-275 |
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10.1007/s41872-024-00259-5 doi (DE-627)SPR057172838 (SPR)s41872-024-00259-5-e DE-627 ger DE-627 rakwb eng 600 VZ 600 VZ Charrak, Naas verfasserin aut Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 Djeddi, Ahmed Zohair verfasserin aut Hafaifa, Ahmed verfasserin (orcid)0000-0002-7812-7429 aut Elbar, Mohammed verfasserin aut Iratni, Abdelhamid verfasserin aut Colak, Ilhami verfasserin aut Enthalten in Life cycle reliability and safety engineering Springer Nature Singapore, 2017 13(2024), 3 vom: 12. Juli, Seite 255-275 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:13 year:2024 number:3 day:12 month:07 pages:255-275 https://dx.doi.org/10.1007/s41872-024-00259-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2024 3 12 07 255-275 |
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10.1007/s41872-024-00259-5 doi (DE-627)SPR057172838 (SPR)s41872-024-00259-5-e DE-627 ger DE-627 rakwb eng 600 VZ 600 VZ Charrak, Naas verfasserin aut Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 Djeddi, Ahmed Zohair verfasserin aut Hafaifa, Ahmed verfasserin (orcid)0000-0002-7812-7429 aut Elbar, Mohammed verfasserin aut Iratni, Abdelhamid verfasserin aut Colak, Ilhami verfasserin aut Enthalten in Life cycle reliability and safety engineering Springer Nature Singapore, 2017 13(2024), 3 vom: 12. Juli, Seite 255-275 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:13 year:2024 number:3 day:12 month:07 pages:255-275 https://dx.doi.org/10.1007/s41872-024-00259-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2024 3 12 07 255-275 |
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10.1007/s41872-024-00259-5 doi (DE-627)SPR057172838 (SPR)s41872-024-00259-5-e DE-627 ger DE-627 rakwb eng 600 VZ 600 VZ Charrak, Naas verfasserin aut Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 Djeddi, Ahmed Zohair verfasserin aut Hafaifa, Ahmed verfasserin (orcid)0000-0002-7812-7429 aut Elbar, Mohammed verfasserin aut Iratni, Abdelhamid verfasserin aut Colak, Ilhami verfasserin aut Enthalten in Life cycle reliability and safety engineering Springer Nature Singapore, 2017 13(2024), 3 vom: 12. Juli, Seite 255-275 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:13 year:2024 number:3 day:12 month:07 pages:255-275 https://dx.doi.org/10.1007/s41872-024-00259-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2024 3 12 07 255-275 |
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10.1007/s41872-024-00259-5 doi (DE-627)SPR057172838 (SPR)s41872-024-00259-5-e DE-627 ger DE-627 rakwb eng 600 VZ 600 VZ Charrak, Naas verfasserin aut Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 Djeddi, Ahmed Zohair verfasserin aut Hafaifa, Ahmed verfasserin (orcid)0000-0002-7812-7429 aut Elbar, Mohammed verfasserin aut Iratni, Abdelhamid verfasserin aut Colak, Ilhami verfasserin aut Enthalten in Life cycle reliability and safety engineering Springer Nature Singapore, 2017 13(2024), 3 vom: 12. Juli, Seite 255-275 (DE-627)887305059 (DE-600)2894228-0 2520-1360 nnns volume:13 year:2024 number:3 day:12 month:07 pages:255-275 https://dx.doi.org/10.1007/s41872-024-00259-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2024 3 12 07 255-275 |
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Charrak, Naas @@aut@@ Djeddi, Ahmed Zohair @@aut@@ Hafaifa, Ahmed @@aut@@ Elbar, Mohammed @@aut@@ Iratni, Abdelhamid @@aut@@ Colak, Ilhami @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. 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Charrak, Naas |
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600 VZ Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data Johnson distributions (dpeaa)DE-He213 SU distribution (dpeaa)DE-He213 Gray Wolf Optimizer (dpeaa)DE-He213 Gas turbine (dpeaa)DE-He213 Reliability data (dpeaa)DE-He213 Data fitting (dpeaa)DE-He213 Weibull distribution (dpeaa)DE-He213 Dependability (dpeaa)DE-He213 |
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johnson’s su distribution using gray wolf optimizer algorithm for fitting gas turbine reliability data |
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Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data |
| abstract |
Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Controlling failures and degradation of gas turbines is crucial for optimizing efficiency, productivity, and maintaining safe operations in the oil and gas industry. Reliability indices play a vital role in supporting these goals by enabling informed decisions about gas turbine lifespan extension and operational safety. This study proposes a novel approach to estimate reliability indices for a GE MS5002C gas turbine. It leverages the Johnson SU distribution applied to operating data and optimizes the obtained model using the Gray Wolf algorithm to improve prediction accuracy. We compare the proposed method with the three-parameter Weibull distribution to validate its effectiveness. By employing the Johnson SU transformation alongside the Gray Wolf Optimizer, this work offers a more accurate and robust method for determining reliability indicators. This approach, based on survival analysis, unlocks the full operating potential of the turbine while addressing uncertainties and errors in reliability modeling. Consequently, it allows for enhanced control of failure sources throughout the turbine's life cycle, ensuring availability and minimizing environmental impact. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Johnson’s SU distribution using Gray Wolf Optimizer algorithm for fitting gas turbine reliability data |
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https://dx.doi.org/10.1007/s41872-024-00259-5 |
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Djeddi, Ahmed Zohair Hafaifa, Ahmed Elbar, Mohammed Iratni, Abdelhamid Colak, Ilhami |
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Djeddi, Ahmed Zohair Hafaifa, Ahmed Elbar, Mohammed Iratni, Abdelhamid Colak, Ilhami |
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10.1007/s41872-024-00259-5 |
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| score |
7.4008074 |