Dynamic prediction of the performance reliability of high-speed railway bearings
Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different serv...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ye, Liang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© The Brazilian Society of Mechanical Sciences and Engineering 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Brazilian Society of Mechanical Sciences and Engineering - Berlin : Springer, 2003, 41(2019), 11 vom: 02. Nov. |
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| Übergeordnetes Werk: |
volume:41 ; year:2019 ; number:11 ; day:02 ; month:11 |
| Links: |
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| DOI / URN: |
10.1007/s40430-019-2041-z |
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| Katalog-ID: |
SPR036464031 |
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| 520 | |a Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. | ||
| 650 | 4 | |a High-speed railway bearings |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Performance reliability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Variation intensity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bootstrap-least squares method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Maximum entropy principle |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Poisson process |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Xia, Xintao |0 (orcid)0000-0002-6872-3788 |4 aut | |
| 700 | 1 | |a Chang, Zhen |4 aut | |
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10.1007/s40430-019-2041-z doi (DE-627)SPR036464031 (SPR)s40430-019-2041-z-e DE-627 ger DE-627 rakwb eng Ye, Liang verfasserin aut Dynamic prediction of the performance reliability of high-speed railway bearings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Society of Mechanical Sciences and Engineering 2019 Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 Xia, Xintao (orcid)0000-0002-6872-3788 aut Chang, Zhen aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 41(2019), 11 vom: 02. Nov. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:41 year:2019 number:11 day:02 month:11 https://dx.doi.org/10.1007/s40430-019-2041-z 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_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 41 2019 11 02 11 |
| spelling |
10.1007/s40430-019-2041-z doi (DE-627)SPR036464031 (SPR)s40430-019-2041-z-e DE-627 ger DE-627 rakwb eng Ye, Liang verfasserin aut Dynamic prediction of the performance reliability of high-speed railway bearings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Society of Mechanical Sciences and Engineering 2019 Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 Xia, Xintao (orcid)0000-0002-6872-3788 aut Chang, Zhen aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 41(2019), 11 vom: 02. Nov. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:41 year:2019 number:11 day:02 month:11 https://dx.doi.org/10.1007/s40430-019-2041-z 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_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 41 2019 11 02 11 |
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10.1007/s40430-019-2041-z doi (DE-627)SPR036464031 (SPR)s40430-019-2041-z-e DE-627 ger DE-627 rakwb eng Ye, Liang verfasserin aut Dynamic prediction of the performance reliability of high-speed railway bearings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Society of Mechanical Sciences and Engineering 2019 Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 Xia, Xintao (orcid)0000-0002-6872-3788 aut Chang, Zhen aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 41(2019), 11 vom: 02. Nov. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:41 year:2019 number:11 day:02 month:11 https://dx.doi.org/10.1007/s40430-019-2041-z 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_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 41 2019 11 02 11 |
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10.1007/s40430-019-2041-z doi (DE-627)SPR036464031 (SPR)s40430-019-2041-z-e DE-627 ger DE-627 rakwb eng Ye, Liang verfasserin aut Dynamic prediction of the performance reliability of high-speed railway bearings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Society of Mechanical Sciences and Engineering 2019 Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 Xia, Xintao (orcid)0000-0002-6872-3788 aut Chang, Zhen aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 41(2019), 11 vom: 02. Nov. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:41 year:2019 number:11 day:02 month:11 https://dx.doi.org/10.1007/s40430-019-2041-z 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_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 41 2019 11 02 11 |
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10.1007/s40430-019-2041-z doi (DE-627)SPR036464031 (SPR)s40430-019-2041-z-e DE-627 ger DE-627 rakwb eng Ye, Liang verfasserin aut Dynamic prediction of the performance reliability of high-speed railway bearings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Brazilian Society of Mechanical Sciences and Engineering 2019 Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 Xia, Xintao (orcid)0000-0002-6872-3788 aut Chang, Zhen aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 41(2019), 11 vom: 02. Nov. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:41 year:2019 number:11 day:02 month:11 https://dx.doi.org/10.1007/s40430-019-2041-z 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_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 41 2019 11 02 11 |
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| author |
Ye, Liang |
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Ye, Liang misc High-speed railway bearings misc Performance reliability misc Variation intensity misc Bootstrap-least squares method misc Maximum entropy principle misc Poisson process Dynamic prediction of the performance reliability of high-speed railway bearings |
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Dynamic prediction of the performance reliability of high-speed railway bearings High-speed railway bearings (dpeaa)DE-He213 Performance reliability (dpeaa)DE-He213 Variation intensity (dpeaa)DE-He213 Bootstrap-least squares method (dpeaa)DE-He213 Maximum entropy principle (dpeaa)DE-He213 Poisson process (dpeaa)DE-He213 |
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misc High-speed railway bearings misc Performance reliability misc Variation intensity misc Bootstrap-least squares method misc Maximum entropy principle misc Poisson process |
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misc High-speed railway bearings misc Performance reliability misc Variation intensity misc Bootstrap-least squares method misc Maximum entropy principle misc Poisson process |
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Dynamic prediction of the performance reliability of high-speed railway bearings |
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dynamic prediction of the performance reliability of high-speed railway bearings |
| title_auth |
Dynamic prediction of the performance reliability of high-speed railway bearings |
| abstract |
Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. © The Brazilian Society of Mechanical Sciences and Engineering 2019 |
| abstractGer |
Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. © The Brazilian Society of Mechanical Sciences and Engineering 2019 |
| abstract_unstemmed |
Abstract Based on the measured vibration status of high-speed railway bearings, a dynamic prediction model of reliability is proposed to realize the real-time monitoring and forecasting of the performance reliability of high-speed railway bearings. The vibration variation intensity of different service periods is quantified and analysed to measure the degree of the potential variation of the bearings. The five closest and updated variation intensities are obtained with linear fitting by the bootstrap-least squares method, and the maximum entropy principle is utilized to forecast the true value and upper and lower bounds of the variation intensity in the subsequent period. Then, the dynamic prediction of reliability for high-speed railway bearings is realized according to the Poisson process. Finally, reliability forecast values are compared with actual values to verify the accuracy and feasibility of the dynamic model. The investigation shows that the maximum relative error of the predicted reliability value is only 5.11% and that the proposed model can precisely predict the performance reliability of the high-speed railway bearings and give timely warning before the reliability decreases or a malignant accident occurs. © The Brazilian Society of Mechanical Sciences and Engineering 2019 |
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| title_short |
Dynamic prediction of the performance reliability of high-speed railway bearings |
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https://dx.doi.org/10.1007/s40430-019-2041-z |
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Xia, Xintao Chang, Zhen |
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| doi_str |
10.1007/s40430-019-2041-z |
| up_date |
2024-07-03T17:46:39.946Z |
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