Datum unit optimization for robustness of a journal bearing diagnosis system
Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jeon, Byung Chul [verfasserIn] Jung, Joon Ha [verfasserIn] Youn, Byeng Dong [verfasserIn] Kim, Yeon-Whan [verfasserIn] Bae, Yong-Chae [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of precision engineering and manufacturing - Sŏul : KSPE, 2009, 16(2015), 11 vom: 01. Okt., Seite 2411-2425 |
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| Übergeordnetes Werk: |
volume:16 ; year:2015 ; number:11 ; day:01 ; month:10 ; pages:2411-2425 |
| Links: |
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| DOI / URN: |
10.1007/s12541-015-0311-y |
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| Katalog-ID: |
SPR026097915 |
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| 245 | 1 | 0 | |a Datum unit optimization for robustness of a journal bearing diagnosis system |
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| 520 | |a Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. | ||
| 650 | 4 | |a Class separation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Datum unit |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Diagnostics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Journal bearing |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Jung, Joon Ha |e verfasserin |4 aut | |
| 700 | 1 | |a Youn, Byeng Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yeon-Whan |e verfasserin |4 aut | |
| 700 | 1 | |a Bae, Yong-Chae |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of precision engineering and manufacturing |d Sŏul : KSPE, 2009 |g 16(2015), 11 vom: 01. Okt., Seite 2411-2425 |w (DE-627)609403109 |w (DE-600)2515436-9 |x 2005-4602 |7 nnns |
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10.1007/s12541-015-0311-y doi (DE-627)SPR026097915 (SPR)s12541-015-0311-y-e DE-627 ger DE-627 rakwb eng 600 ASE Jeon, Byung Chul verfasserin aut Datum unit optimization for robustness of a journal bearing diagnosis system 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. Class separation (dpeaa)DE-He213 Datum unit (dpeaa)DE-He213 Diagnostics (dpeaa)DE-He213 Journal bearing (dpeaa)DE-He213 Jung, Joon Ha verfasserin aut Youn, Byeng Dong verfasserin aut Kim, Yeon-Whan verfasserin aut Bae, Yong-Chae verfasserin aut Enthalten in International journal of precision engineering and manufacturing Sŏul : KSPE, 2009 16(2015), 11 vom: 01. Okt., Seite 2411-2425 (DE-627)609403109 (DE-600)2515436-9 2005-4602 nnns volume:16 year:2015 number:11 day:01 month:10 pages:2411-2425 https://dx.doi.org/10.1007/s12541-015-0311-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2015 11 01 10 2411-2425 |
| spelling |
10.1007/s12541-015-0311-y doi (DE-627)SPR026097915 (SPR)s12541-015-0311-y-e DE-627 ger DE-627 rakwb eng 600 ASE Jeon, Byung Chul verfasserin aut Datum unit optimization for robustness of a journal bearing diagnosis system 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. Class separation (dpeaa)DE-He213 Datum unit (dpeaa)DE-He213 Diagnostics (dpeaa)DE-He213 Journal bearing (dpeaa)DE-He213 Jung, Joon Ha verfasserin aut Youn, Byeng Dong verfasserin aut Kim, Yeon-Whan verfasserin aut Bae, Yong-Chae verfasserin aut Enthalten in International journal of precision engineering and manufacturing Sŏul : KSPE, 2009 16(2015), 11 vom: 01. Okt., Seite 2411-2425 (DE-627)609403109 (DE-600)2515436-9 2005-4602 nnns volume:16 year:2015 number:11 day:01 month:10 pages:2411-2425 https://dx.doi.org/10.1007/s12541-015-0311-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2015 11 01 10 2411-2425 |
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10.1007/s12541-015-0311-y doi (DE-627)SPR026097915 (SPR)s12541-015-0311-y-e DE-627 ger DE-627 rakwb eng 600 ASE Jeon, Byung Chul verfasserin aut Datum unit optimization for robustness of a journal bearing diagnosis system 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. Class separation (dpeaa)DE-He213 Datum unit (dpeaa)DE-He213 Diagnostics (dpeaa)DE-He213 Journal bearing (dpeaa)DE-He213 Jung, Joon Ha verfasserin aut Youn, Byeng Dong verfasserin aut Kim, Yeon-Whan verfasserin aut Bae, Yong-Chae verfasserin aut Enthalten in International journal of precision engineering and manufacturing Sŏul : KSPE, 2009 16(2015), 11 vom: 01. Okt., Seite 2411-2425 (DE-627)609403109 (DE-600)2515436-9 2005-4602 nnns volume:16 year:2015 number:11 day:01 month:10 pages:2411-2425 https://dx.doi.org/10.1007/s12541-015-0311-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2015 11 01 10 2411-2425 |
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10.1007/s12541-015-0311-y doi (DE-627)SPR026097915 (SPR)s12541-015-0311-y-e DE-627 ger DE-627 rakwb eng 600 ASE Jeon, Byung Chul verfasserin aut Datum unit optimization for robustness of a journal bearing diagnosis system 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. Class separation (dpeaa)DE-He213 Datum unit (dpeaa)DE-He213 Diagnostics (dpeaa)DE-He213 Journal bearing (dpeaa)DE-He213 Jung, Joon Ha verfasserin aut Youn, Byeng Dong verfasserin aut Kim, Yeon-Whan verfasserin aut Bae, Yong-Chae verfasserin aut Enthalten in International journal of precision engineering and manufacturing Sŏul : KSPE, 2009 16(2015), 11 vom: 01. Okt., Seite 2411-2425 (DE-627)609403109 (DE-600)2515436-9 2005-4602 nnns volume:16 year:2015 number:11 day:01 month:10 pages:2411-2425 https://dx.doi.org/10.1007/s12541-015-0311-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2015 11 01 10 2411-2425 |
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10.1007/s12541-015-0311-y doi (DE-627)SPR026097915 (SPR)s12541-015-0311-y-e DE-627 ger DE-627 rakwb eng 600 ASE Jeon, Byung Chul verfasserin aut Datum unit optimization for robustness of a journal bearing diagnosis system 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. Class separation (dpeaa)DE-He213 Datum unit (dpeaa)DE-He213 Diagnostics (dpeaa)DE-He213 Journal bearing (dpeaa)DE-He213 Jung, Joon Ha verfasserin aut Youn, Byeng Dong verfasserin aut Kim, Yeon-Whan verfasserin aut Bae, Yong-Chae verfasserin aut Enthalten in International journal of precision engineering and manufacturing Sŏul : KSPE, 2009 16(2015), 11 vom: 01. Okt., Seite 2411-2425 (DE-627)609403109 (DE-600)2515436-9 2005-4602 nnns volume:16 year:2015 number:11 day:01 month:10 pages:2411-2425 https://dx.doi.org/10.1007/s12541-015-0311-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_2008 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 16 2015 11 01 10 2411-2425 |
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International journal of precision engineering and manufacturing |
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Jeon, Byung Chul @@aut@@ Jung, Joon Ha @@aut@@ Youn, Byeng Dong @@aut@@ Kim, Yeon-Whan @@aut@@ Bae, Yong-Chae @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR026097915</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111133353.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12541-015-0311-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026097915</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12541-015-0311-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jeon, Byung Chul</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Datum unit optimization for robustness of a journal bearing diagnosis system</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. 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datum unit optimization for robustness of a journal bearing diagnosis system |
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Datum unit optimization for robustness of a journal bearing diagnosis system |
| abstract |
Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. |
| abstractGer |
Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. |
| abstract_unstemmed |
Abstract This paper describes a robust diagnosis method for a rotor system with a journal bearing. To enhance the robustness of a journal bearing diagnosis system, it is of great importance to define an optimum datum unit for featuring anomaly states of the rotor system. To support the research goal, this study makes use of three measures for class separation, including Kullback-Leibler divergence (KLD), Fisher discriminant ratio (FDR), and a newly proposed measure: probability of separation (PoS). From the viewpoint of class separability, this work found that PoS is more attractive than other methods for quantification of class separation. PoS offers favorable properties like normalization, boundedness, and high sensitivity. A generic algorithm integrated with one of three measures consistently suggested the optimum datum units among the feasible datum units. Optimum datum units were found to be one-cycle for time-domain features and sixty-cycles for frequency-domain features. The support vector machine (SVM) classifier with the optimum datum units was used for diagnosing a normal and three anomaly states. The health classification results showed that the proposed optimum datum units can outperform other datum units. |
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Datum unit optimization for robustness of a journal bearing diagnosis system |
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https://dx.doi.org/10.1007/s12541-015-0311-y |
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Jung, Joon Ha Youn, Byeng Dong Kim, Yeon-Whan Bae, Yong-Chae |
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Jung, Joon Ha Youn, Byeng Dong Kim, Yeon-Whan Bae, Yong-Chae |
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10.1007/s12541-015-0311-y |
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2024-07-03T18:52:32.873Z |
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| score |
7.3991985 |