Safety justification of train movement dynamic processes using evidence theory and reference models
•The inference approach using evidence theory and reference models is proposed.•The proposed approach is used for safety justification of train movements.•The mass functions are defined based on the intersection of operation time intervals.•The case study testifies the proposed approach about the pr...
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Veröffentlicht in: | Knowledge-based systems 2018-01, Vol.139, p.78-88 |
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creator | Zhou, Yonghua Tao, Xin Luan, Lei Wang, Zhihui |
description | •The inference approach using evidence theory and reference models is proposed.•The proposed approach is used for safety justification of train movements.•The mass functions are defined based on the intersection of operation time intervals.•The case study testifies the proposed approach about the prevention of 7/23 railway accident.•The proposed approach provides a way to diagnose fault equipment.
The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems. |
doi_str_mv | 10.1016/j.knosys.2017.10.012 |
format | Article |
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The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems.</description><identifier>ISSN: 0950-7051</identifier><identifier>EISSN: 1872-7409</identifier><identifier>DOI: 10.1016/j.knosys.2017.10.012</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Aberration ; Case studies ; Control systems ; Emergency procedures ; Evidence theory ; Fault diagnosis ; Ground based control ; Inference ; Knowledge representation ; Railroad accidents & safety ; Railway accidents ; Reference model ; Safety ; Safety analysis ; Train movement ; Trains</subject><ispartof>Knowledge-based systems, 2018-01, Vol.139, p.78-88</ispartof><rights>2017 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Jan 1, 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c334t-8c0faed135ebdfae8b983d5a223a012dfe6bd280219ab5daf6beb949b47d45c03</citedby><cites>FETCH-LOGICAL-c334t-8c0faed135ebdfae8b983d5a223a012dfe6bd280219ab5daf6beb949b47d45c03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.knosys.2017.10.012$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Zhou, Yonghua</creatorcontrib><creatorcontrib>Tao, Xin</creatorcontrib><creatorcontrib>Luan, Lei</creatorcontrib><creatorcontrib>Wang, Zhihui</creatorcontrib><title>Safety justification of train movement dynamic processes using evidence theory and reference models</title><title>Knowledge-based systems</title><description>•The inference approach using evidence theory and reference models is proposed.•The proposed approach is used for safety justification of train movements.•The mass functions are defined based on the intersection of operation time intervals.•The case study testifies the proposed approach about the prevention of 7/23 railway accident.•The proposed approach provides a way to diagnose fault equipment.
The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems.</description><subject>Aberration</subject><subject>Case studies</subject><subject>Control systems</subject><subject>Emergency procedures</subject><subject>Evidence theory</subject><subject>Fault diagnosis</subject><subject>Ground based control</subject><subject>Inference</subject><subject>Knowledge representation</subject><subject>Railroad accidents & safety</subject><subject>Railway accidents</subject><subject>Reference model</subject><subject>Safety</subject><subject>Safety analysis</subject><subject>Train movement</subject><subject>Trains</subject><issn>0950-7051</issn><issn>1872-7409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9UEtLxDAQDqLguvoPPAQ8tyZpu00vgiy-YMGDeg5pMtXUbbImqdB_b2o9e5rhY-Z7IXRJSU4J3Vz3-ad1YQo5I7ROUE4oO0IrymuW1SVpjtGKNBXJalLRU3QWQk8IYYzyFVIvsoM44X4M0XRGyWicxa7D0Utj8eC-YQAbsZ6sHIzCB-8UhAABj8HYdwzfRoNVgOMHOD9haTX20IH_BQenYR_O0Ukn9wEu_uYavd3fvW4fs93zw9P2dpepoihjxhXpJGhaVNDqtPG24YWuJGOFTIF0B5tWM04YbWRbadltWmibsmnLWpeVIsUaXS28yeTXCCGK3o3eJklBG17WNSkqnq7K5Up5F0LyKg7eDNJPghIx1yl6sdQp5jpnNKmnt5vlLQVKocGLoMwcUhsPKgrtzP8EP0mtg1I</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Zhou, Yonghua</creator><creator>Tao, Xin</creator><creator>Luan, Lei</creator><creator>Wang, Zhihui</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>E3H</scope><scope>F2A</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20180101</creationdate><title>Safety justification of train movement dynamic processes using evidence theory and reference models</title><author>Zhou, Yonghua ; Tao, Xin ; Luan, Lei ; Wang, Zhihui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-8c0faed135ebdfae8b983d5a223a012dfe6bd280219ab5daf6beb949b47d45c03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Aberration</topic><topic>Case studies</topic><topic>Control systems</topic><topic>Emergency procedures</topic><topic>Evidence theory</topic><topic>Fault diagnosis</topic><topic>Ground based control</topic><topic>Inference</topic><topic>Knowledge representation</topic><topic>Railroad accidents & safety</topic><topic>Railway accidents</topic><topic>Reference model</topic><topic>Safety</topic><topic>Safety analysis</topic><topic>Train movement</topic><topic>Trains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Yonghua</creatorcontrib><creatorcontrib>Tao, Xin</creatorcontrib><creatorcontrib>Luan, Lei</creatorcontrib><creatorcontrib>Wang, Zhihui</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Knowledge-based systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Yonghua</au><au>Tao, Xin</au><au>Luan, Lei</au><au>Wang, Zhihui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Safety justification of train movement dynamic processes using evidence theory and reference models</atitle><jtitle>Knowledge-based systems</jtitle><date>2018-01-01</date><risdate>2018</risdate><volume>139</volume><spage>78</spage><epage>88</epage><pages>78-88</pages><issn>0950-7051</issn><eissn>1872-7409</eissn><abstract>•The inference approach using evidence theory and reference models is proposed.•The proposed approach is used for safety justification of train movements.•The mass functions are defined based on the intersection of operation time intervals.•The case study testifies the proposed approach about the prevention of 7/23 railway accident.•The proposed approach provides a way to diagnose fault equipment.
The efficient solution to justify train movement safety is to analyze train movement situations via train operation knowledge and knowledge-based inference tools. In this paper, train operation knowledge is represented as train movement models and conditions, collectively called rule-based train movement reference models. The Dempster–Shafer (D–S) evidence theory is employed to infer the model and condition under which a train is running. Consequently, aberrant models and conditions, potentially endangering train operation safety, are identified in advance so that emergency measures can be taken to prevent train operation accidents. The mass function is defined as the approximation level of the train operation time interval within one block section of a railway line to that obtained from various reference models. The D–S theory is also applied to train movement dynamic processes to gradually identify train operation situations, using the combined section and process mass functions. The proposed inference approach using evidence theory and reference models (ETRM) qualitatively and quantitatively judges the rationalities of train operation control logic and variation tendencies. A case study to prevent the occurrence of the 7/23 railway accident in China demonstrates the validity of the proposed inference approach using ETRM. The analysis and inference centering on train movement situations can meanwhile diagnose the operation status of train onboard and ground control systems.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.knosys.2017.10.012</doi><tpages>11</tpages></addata></record> |
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subjects | Aberration Case studies Control systems Emergency procedures Evidence theory Fault diagnosis Ground based control Inference Knowledge representation Railroad accidents & safety Railway accidents Reference model Safety Safety analysis Train movement Trains |
title | Safety justification of train movement dynamic processes using evidence theory and reference models |
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