Classification of spring strain signals for road classes using Hilbert–Huang transform

Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In th...
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Autor*in:	Kong, Y. S. [verfasserIn] Abdullah, S. Singh, S. S. K.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Strain–displacement Hilbert–Huang transform K-nearest neighbours Road classes

Anmerkung:	© The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022

Übergeordnetes Werk:	Enthalten in: Journal of the Brazilian Society of Mechanical Sciences and Engineering - Berlin : Springer, 2003, 44(2022), 3 vom: 16. Feb.
Übergeordnetes Werk:	volume:44 ; year:2022 ; number:3 ; day:16 ; month:02

Links:	Volltext

DOI / URN:	10.1007/s40430-022-03390-5

Katalog-ID:	SPR046255834

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520			\|a Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design.
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allfields	10.1007/s40430-022-03390-5 doi (DE-627)SPR046255834 (SPR)s40430-022-03390-5-e DE-627 ger DE-627 rakwb eng Kong, Y. S. verfasserin (orcid)0000-0003-4201-266X aut Classification of spring strain signals for road classes using Hilbert–Huang transform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022 Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213 Abdullah, S. aut Singh, S. S. K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 44(2022), 3 vom: 16. Feb. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:44 year:2022 number:3 day:16 month:02 https://dx.doi.org/10.1007/s40430-022-03390-5 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_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 44 2022 3 16 02
spelling	10.1007/s40430-022-03390-5 doi (DE-627)SPR046255834 (SPR)s40430-022-03390-5-e DE-627 ger DE-627 rakwb eng Kong, Y. S. verfasserin (orcid)0000-0003-4201-266X aut Classification of spring strain signals for road classes using Hilbert–Huang transform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022 Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213 Abdullah, S. aut Singh, S. S. K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 44(2022), 3 vom: 16. Feb. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:44 year:2022 number:3 day:16 month:02 https://dx.doi.org/10.1007/s40430-022-03390-5 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_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 44 2022 3 16 02
allfields_unstemmed	10.1007/s40430-022-03390-5 doi (DE-627)SPR046255834 (SPR)s40430-022-03390-5-e DE-627 ger DE-627 rakwb eng Kong, Y. S. verfasserin (orcid)0000-0003-4201-266X aut Classification of spring strain signals for road classes using Hilbert–Huang transform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022 Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213 Abdullah, S. aut Singh, S. S. K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 44(2022), 3 vom: 16. Feb. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:44 year:2022 number:3 day:16 month:02 https://dx.doi.org/10.1007/s40430-022-03390-5 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_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 44 2022 3 16 02
allfieldsGer	10.1007/s40430-022-03390-5 doi (DE-627)SPR046255834 (SPR)s40430-022-03390-5-e DE-627 ger DE-627 rakwb eng Kong, Y. S. verfasserin (orcid)0000-0003-4201-266X aut Classification of spring strain signals for road classes using Hilbert–Huang transform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022 Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213 Abdullah, S. aut Singh, S. S. K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 44(2022), 3 vom: 16. Feb. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:44 year:2022 number:3 day:16 month:02 https://dx.doi.org/10.1007/s40430-022-03390-5 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_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 44 2022 3 16 02
allfieldsSound	10.1007/s40430-022-03390-5 doi (DE-627)SPR046255834 (SPR)s40430-022-03390-5-e DE-627 ger DE-627 rakwb eng Kong, Y. S. verfasserin (orcid)0000-0003-4201-266X aut Classification of spring strain signals for road classes using Hilbert–Huang transform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022 Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213 Abdullah, S. aut Singh, S. S. K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 44(2022), 3 vom: 16. Feb. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:44 year:2022 number:3 day:16 month:02 https://dx.doi.org/10.1007/s40430-022-03390-5 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_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 44 2022 3 16 02
language	English
source	Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering 44(2022), 3 vom: 16. Feb. volume:44 year:2022 number:3 day:16 month:02
sourceStr	Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering 44(2022), 3 vom: 16. Feb. volume:44 year:2022 number:3 day:16 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Strain–displacement Hilbert–Huang transform K-nearest neighbours Road classes
isfreeaccess_bool	false
container_title	Journal of the Brazilian Society of Mechanical Sciences and Engineering
authorswithroles_txt_mv	Kong, Y. S. @@aut@@ Abdullah, S. @@aut@@ Singh, S. S. K. @@aut@@
publishDateDaySort_date	2022-02-16T00:00:00Z
hierarchy_top_id	387477950
id	SPR046255834
language_de	englisch
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author	Kong, Y. S.
spellingShingle	Kong, Y. S. misc Strain–displacement misc Hilbert–Huang transform misc K-nearest neighbours misc Road classes Classification of spring strain signals for road classes using Hilbert–Huang transform
authorStr	Kong, Y. S.
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topic_title	Classification of spring strain signals for road classes using Hilbert–Huang transform Strain–displacement (dpeaa)DE-He213 Hilbert–Huang transform (dpeaa)DE-He213 K-nearest neighbours (dpeaa)DE-He213 Road classes (dpeaa)DE-He213
topic	misc Strain–displacement misc Hilbert–Huang transform misc K-nearest neighbours misc Road classes
topic_unstemmed	misc Strain–displacement misc Hilbert–Huang transform misc K-nearest neighbours misc Road classes
topic_browse	misc Strain–displacement misc Hilbert–Huang transform misc K-nearest neighbours misc Road classes
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title	Classification of spring strain signals for road classes using Hilbert–Huang transform
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title_full	Classification of spring strain signals for road classes using Hilbert–Huang transform
author_sort	Kong, Y. S.
journal	Journal of the Brazilian Society of Mechanical Sciences and Engineering
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author_browse	Kong, Y. S. Abdullah, S. Singh, S. S. K.
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author-letter	Kong, Y. S.
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title_sort	classification of spring strain signals for road classes using hilbert–huang transform
title_auth	Classification of spring strain signals for road classes using Hilbert–Huang transform
abstract	Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022
abstractGer	Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022
abstract_unstemmed	Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2022
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title_short	Classification of spring strain signals for road classes using Hilbert–Huang transform
url	https://dx.doi.org/10.1007/s40430-022-03390-5
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author2	Abdullah, S. Singh, S. S. K.
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up_date	2024-07-03T21:22:59.798Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046255834</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507124927.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220217s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40430-022-03390-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046255834</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40430-022-03390-5-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">Kong, Y. S.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-4201-266X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of spring strain signals for road classes using Hilbert–Huang transform</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 The Brazilian Society of Mechanical Sciences and Engineering 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a real-time classification of road class based on the measured spring strain signals through Hilbert–Huang transform (HHT). Road profile measurement is an important element for vehicle ride and fatigue assessments, but the actual measurement procedures are complex. In this analysis, a finite element model was used to obtain the strain–displacement function. HHT was applied to obtain the instantaneous energies and frequencies from the spring displacement signals. The obtained instantaneous frequencies and energies were used to classify the road class according to ISO 8608 standard. For validation purpose, five sets of road class were generated and estimated using the proposed algorithm. Subsequently, the data were classify using K-nearest neighbours approach and an accuracy of 99.3% was achieved. This work offers an alternative solution for road class detection using strain measurements which is significant for automotive component design.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Strain–displacement</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hilbert–Huang transform</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-nearest neighbours</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Road classes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abdullah, S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, S. S. K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of the Brazilian Society of Mechanical Sciences and Engineering</subfield><subfield code="d">Berlin : Springer, 2003</subfield><subfield code="g">44(2022), 3 vom: 16. 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Classification of spring strain signals for road classes using Hilbert–Huang transform

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