Remaining useful life prediction of silicone foam using matching pursuit and a particle filter
Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit a...
Ausführliche Beschreibung
Autor*in: |
Wang, Jiulong [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Übergeordnetes Werk: |
Enthalten in: Mechanics of time-dependent materials - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997, 27(2022), 3 vom: 14. Juli, Seite 791-804 |
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Übergeordnetes Werk: |
volume:27 ; year:2022 ; number:3 ; day:14 ; month:07 ; pages:791-804 |
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DOI / URN: |
10.1007/s11043-022-09562-z |
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Katalog-ID: |
SPR053108256 |
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520 | |a Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. | ||
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650 | 4 | |a Remaining useful life prediction |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Sheng, Junjie |4 aut | |
700 | 1 | |a Zhang, Sicai |4 aut | |
700 | 1 | |a Jiang, Jiayong |4 aut | |
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10.1007/s11043-022-09562-z doi (DE-627)SPR053108256 (SPR)s11043-022-09562-z-e DE-627 ger DE-627 rakwb eng Wang, Jiulong verfasserin (orcid)0000-0003-3277-5225 aut Remaining useful life prediction of silicone foam using matching pursuit and a particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. Silicone foam (dpeaa)DE-He213 Remaining useful life prediction (dpeaa)DE-He213 Matching pursuit (dpeaa)DE-He213 Particle filter (dpeaa)DE-He213 Failure mechanism (dpeaa)DE-He213 Sheng, Junjie aut Zhang, Sicai aut Jiang, Jiayong aut Enthalten in Mechanics of time-dependent materials Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 27(2022), 3 vom: 14. Juli, Seite 791-804 (DE-627)311009964 (DE-600)2003935-9 1573-2738 nnns volume:27 year:2022 number:3 day:14 month:07 pages:791-804 https://dx.doi.org/10.1007/s11043-022-09562-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_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_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_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_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 27 2022 3 14 07 791-804 |
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10.1007/s11043-022-09562-z doi (DE-627)SPR053108256 (SPR)s11043-022-09562-z-e DE-627 ger DE-627 rakwb eng Wang, Jiulong verfasserin (orcid)0000-0003-3277-5225 aut Remaining useful life prediction of silicone foam using matching pursuit and a particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. Silicone foam (dpeaa)DE-He213 Remaining useful life prediction (dpeaa)DE-He213 Matching pursuit (dpeaa)DE-He213 Particle filter (dpeaa)DE-He213 Failure mechanism (dpeaa)DE-He213 Sheng, Junjie aut Zhang, Sicai aut Jiang, Jiayong aut Enthalten in Mechanics of time-dependent materials Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 27(2022), 3 vom: 14. Juli, Seite 791-804 (DE-627)311009964 (DE-600)2003935-9 1573-2738 nnns volume:27 year:2022 number:3 day:14 month:07 pages:791-804 https://dx.doi.org/10.1007/s11043-022-09562-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_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_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_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_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 27 2022 3 14 07 791-804 |
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10.1007/s11043-022-09562-z doi (DE-627)SPR053108256 (SPR)s11043-022-09562-z-e DE-627 ger DE-627 rakwb eng Wang, Jiulong verfasserin (orcid)0000-0003-3277-5225 aut Remaining useful life prediction of silicone foam using matching pursuit and a particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. Silicone foam (dpeaa)DE-He213 Remaining useful life prediction (dpeaa)DE-He213 Matching pursuit (dpeaa)DE-He213 Particle filter (dpeaa)DE-He213 Failure mechanism (dpeaa)DE-He213 Sheng, Junjie aut Zhang, Sicai aut Jiang, Jiayong aut Enthalten in Mechanics of time-dependent materials Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 27(2022), 3 vom: 14. Juli, Seite 791-804 (DE-627)311009964 (DE-600)2003935-9 1573-2738 nnns volume:27 year:2022 number:3 day:14 month:07 pages:791-804 https://dx.doi.org/10.1007/s11043-022-09562-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_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_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_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_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 27 2022 3 14 07 791-804 |
allfieldsGer |
10.1007/s11043-022-09562-z doi (DE-627)SPR053108256 (SPR)s11043-022-09562-z-e DE-627 ger DE-627 rakwb eng Wang, Jiulong verfasserin (orcid)0000-0003-3277-5225 aut Remaining useful life prediction of silicone foam using matching pursuit and a particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. Silicone foam (dpeaa)DE-He213 Remaining useful life prediction (dpeaa)DE-He213 Matching pursuit (dpeaa)DE-He213 Particle filter (dpeaa)DE-He213 Failure mechanism (dpeaa)DE-He213 Sheng, Junjie aut Zhang, Sicai aut Jiang, Jiayong aut Enthalten in Mechanics of time-dependent materials Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 27(2022), 3 vom: 14. Juli, Seite 791-804 (DE-627)311009964 (DE-600)2003935-9 1573-2738 nnns volume:27 year:2022 number:3 day:14 month:07 pages:791-804 https://dx.doi.org/10.1007/s11043-022-09562-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_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_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_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_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 27 2022 3 14 07 791-804 |
allfieldsSound |
10.1007/s11043-022-09562-z doi (DE-627)SPR053108256 (SPR)s11043-022-09562-z-e DE-627 ger DE-627 rakwb eng Wang, Jiulong verfasserin (orcid)0000-0003-3277-5225 aut Remaining useful life prediction of silicone foam using matching pursuit and a particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. Silicone foam (dpeaa)DE-He213 Remaining useful life prediction (dpeaa)DE-He213 Matching pursuit (dpeaa)DE-He213 Particle filter (dpeaa)DE-He213 Failure mechanism (dpeaa)DE-He213 Sheng, Junjie aut Zhang, Sicai aut Jiang, Jiayong aut Enthalten in Mechanics of time-dependent materials Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 27(2022), 3 vom: 14. Juli, Seite 791-804 (DE-627)311009964 (DE-600)2003935-9 1573-2738 nnns volume:27 year:2022 number:3 day:14 month:07 pages:791-804 https://dx.doi.org/10.1007/s11043-022-09562-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_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_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_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_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 27 2022 3 14 07 791-804 |
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Wang, Jiulong @@aut@@ Sheng, Junjie @@aut@@ Zhang, Sicai @@aut@@ Jiang, Jiayong @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053108256</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230919064654.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230919s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11043-022-09562-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053108256</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11043-022-09562-z-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="100" ind1="1" ind2=" "><subfield code="a">Wang, Jiulong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3277-5225</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Remaining useful life prediction of silicone foam using matching pursuit and a particle filter</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature B.V. 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. 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remaining useful life prediction of silicone foam using matching pursuit and a particle filter |
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Remaining useful life prediction of silicone foam using matching pursuit and a particle filter |
abstract |
Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
abstractGer |
Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
abstract_unstemmed |
Abstract The silicone foam remaining useful life (RUL) predictions have been developed rapidly. However, few of them refer to the degradation model and failure mechanism of silicone foam, leading to unreliable prediction results. To overcome this shortcoming, a method based on the matching pursuit and particle filter (MP-PF) is proposed in this paper. To date, it has been difficult to describe the degradation of silicone foam material without obtaining the property damage parameters. We review the failure mechanism of silicone foams and describe the matching pursuit (MP) method which is applied to reveal the relative contribution of each failure mechanism and adaptively separate physical and chemical relaxation. Through simulation experiments, we illustrate the effectiveness of the proposed method. On this basis, the RUL of silicone foam is predicted by the particle filter (PF) method, which takes full account of the uncertainty of the product and test data. The experimental data shows that the predicted degradation trends for silicone foam match well with the experimental data. In addition, compared with the methods in the literature, the proposed MP-PF method has higher prediction accuracy and can be used to better characterize the degradation of silicon foam, which is of great significance for product design optimization. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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title_short |
Remaining useful life prediction of silicone foam using matching pursuit and a particle filter |
url |
https://dx.doi.org/10.1007/s11043-022-09562-z |
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author2 |
Sheng, Junjie Zhang, Sicai Jiang, Jiayong |
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Sheng, Junjie Zhang, Sicai Jiang, Jiayong |
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doi_str |
10.1007/s11043-022-09562-z |
up_date |
2024-07-03T17:07:13.529Z |
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|
score |
7.399047 |