BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework
The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration p...
Ausführliche Beschreibung
Autor*in: |
Yaqin Yang [verfasserIn] Peng Xu [verfasserIn] Guotao Yang [verfasserIn] Long Chen [verfasserIn] Junbo Li [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
maintenance activities identification |
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Übergeordnetes Werk: |
In: Frontiers in Materials - Frontiers Media S.A., 2014, 8(2021) |
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Übergeordnetes Werk: |
volume:8 ; year:2021 |
Links: |
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DOI / URN: |
10.3389/fmats.2021.620484 |
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Katalog-ID: |
DOAJ058660380 |
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10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 |
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10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 |
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10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 |
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10.3389/fmats.2021.620484 doi (DE-627)DOAJ058660380 (DE-599)DOAJ0fcbe36a02714911aa5be3aa4e08429f DE-627 ger DE-627 rakwb eng Yaqin Yang verfasserin aut BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. maintenance activities identification Bayesian information criterion track irregularity adaptive thresholding dynamic programming Technology T Peng Xu verfasserin aut Guotao Yang verfasserin aut Long Chen verfasserin aut Junbo Li verfasserin aut In Frontiers in Materials Frontiers Media S.A., 2014 8(2021) (DE-627)779920716 (DE-600)2759394-0 22968016 nnns volume:8 year:2021 https://doi.org/10.3389/fmats.2021.620484 kostenfrei https://doaj.org/article/0fcbe36a02714911aa5be3aa4e08429f kostenfrei https://www.frontiersin.org/articles/10.3389/fmats.2021.620484/full kostenfrei https://doaj.org/toc/2296-8016 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 |
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abstract |
The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. |
abstractGer |
The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. |
abstract_unstemmed |
The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections. |
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BIC-Based Data-Driven Rail Track Deterioration Adaptive Piecewise Modeling Framework |
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|
score |
7.400199 |