{"id":"https://openalex.org/W4410308672","doi":"https://doi.org/10.3390/bdcc9050127","title":"Rail Surface Defect Diagnosis Based on Image\u2013Vibration Multimodal Data Fusion","display_name":"Rail Surface Defect Diagnosis Based on Image\u2013Vibration Multimodal Data Fusion","publication_year":2025,"publication_date":"2025-05-12","ids":{"openalex":"https://openalex.org/W4410308672","doi":"https://doi.org/10.3390/bdcc9050127"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc9050127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9050127","pdf_url":"https://www.mdpi.com/2504-2289/9/5/127/pdf?version=1747037871","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-2289/9/5/127/pdf?version=1747037871","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101980926","display_name":"Zhongmei Wang","orcid":"https://orcid.org/0000-0003-0031-0925"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhongmei Wang","raw_affiliation_strings":["College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"],"affiliations":[{"raw_affiliation_string":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108008219","display_name":"Shaoliang Peng","orcid":"https://orcid.org/0000-0002-4647-2615"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenao Peng","raw_affiliation_strings":["College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"],"affiliations":[{"raw_affiliation_string":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057643935","display_name":"Weiwei Ao","orcid":"https://orcid.org/0000-0002-9058-1683"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxiu Ao","raw_affiliation_strings":["College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"],"affiliations":[{"raw_affiliation_string":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405318","display_name":"Jianhua Liu","orcid":"https://orcid.org/0000-0002-1694-0975"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Liu","raw_affiliation_strings":["College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"],"affiliations":[{"raw_affiliation_string":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028633038","display_name":"Changfan Zhang","orcid":"https://orcid.org/0000-0002-9800-3984"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changfan Zhang","raw_affiliation_strings":["College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China"],"affiliations":[{"raw_affiliation_string":"College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China","institution_ids":["https://openalex.org/I49934816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101980926"],"corresponding_institution_ids":["https://openalex.org/I49934816"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":1.1874,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79702507,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"9","issue":"5","first_page":"127","last_page":"127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5638568997383118},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.5433346629142761},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5271776914596558},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4854746162891388},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4854357838630676},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4829094409942627},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4584142863750458},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.45748889446258545},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3960038423538208},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.20565709471702576},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.17399996519088745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10861599445343018}],"concepts":[{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5638568997383118},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.5433346629142761},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5271776914596558},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4854746162891388},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4854357838630676},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4829094409942627},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4584142863750458},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.45748889446258545},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3960038423538208},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.20565709471702576},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.17399996519088745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10861599445343018},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/bdcc9050127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9050127","pdf_url":"https://www.mdpi.com/2504-2289/9/5/127/pdf?version=1747037871","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f3576dad6a0c4a26bbdb3dc36abed828","is_oa":true,"landing_page_url":"https://doaj.org/article/f3576dad6a0c4a26bbdb3dc36abed828","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 9, Iss 5, p 127 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/bdcc9050127","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc9050127","pdf_url":"https://www.mdpi.com/2504-2289/9/5/127/pdf?version=1747037871","source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1661844034","display_name":null,"funder_award_id":"62106074","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2097151789","display_name":null,"funder_award_id":"2021YFF0501101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5393611267","display_name":null,"funder_award_id":"52272347","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6925505101","display_name":null,"funder_award_id":"2024JJ7132","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7711738205","display_name":null,"funder_award_id":"2021YFF0501101","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410308672.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W2076063813","https://openalex.org/W2530133016","https://openalex.org/W2752782242","https://openalex.org/W2919115771","https://openalex.org/W3002301267","https://openalex.org/W3034320133","https://openalex.org/W4285505265","https://openalex.org/W4360993861","https://openalex.org/W4367016529","https://openalex.org/W4385268906","https://openalex.org/W4391037910","https://openalex.org/W4392165123","https://openalex.org/W4392438943","https://openalex.org/W4394611611","https://openalex.org/W4400042547","https://openalex.org/W4400579218","https://openalex.org/W4401545156","https://openalex.org/W4408210424","https://openalex.org/W6869954207"],"related_works":["https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W2788731446","https://openalex.org/W2204403038","https://openalex.org/W3214791684","https://openalex.org/W3152170969","https://openalex.org/W2379054866","https://openalex.org/W2370195708","https://openalex.org/W1490651872","https://openalex.org/W2139242969"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,30,127,162],"challenges":[3],"in":[4,18,172],"existing":[5],"multi-sensor":[6],"data":[7,26],"fusion":[8,96],"methods":[9],"for":[10],"rail":[11,152,173],"surface":[12,153,174],"defect":[13,154,175],"diagnosis,":[14],"particularly":[15],"their":[16],"limitations":[17],"fully":[19,143],"exploiting":[20,144],"potential":[21],"synergistic":[22,104],"information":[23,105],"among":[24],"multimodal":[25,72,140],"and":[27,62,76,114],"effectively":[28],"bridging":[29],"semantic":[31,146],"gap":[32],"between":[33],"heterogeneous":[34],"multi-source":[35],"data,":[36],"this":[37],"paper":[38],"proposes":[39],"a":[40,45,80,94,110],"diagnostic":[41,167],"approach":[42],"based":[43],"on":[44,150],"Progressive":[46],"Joint":[47],"Representation":[48],"Graph":[49],"Attention":[50],"Fusion":[51],"Network":[52],"(PJR-GAFN).":[53],"The":[54],"methodology":[55],"comprises":[56],"five":[57],"principal":[58],"phases:":[59],"Firstly,":[60],"shared":[61,75],"specific":[63],"autoencoders":[64],"are":[65,117,136],"used":[66],"to":[67,85,100,119,138],"extract":[68],"joint":[69],"representations":[70],"of":[71,132],"features":[73,88,122],"through":[74],"modality-specific":[77],"representations.":[78],"Secondly,":[79],"squeeze-and-excitation":[81],"module":[82,97],"is":[83,98],"implemented":[84],"amplify":[86],"defect-related":[87],"while":[89],"suppressing":[90],"non-essential":[91],"characteristics.":[92],"Thirdly,":[93],"progressive":[95],"introduced":[99],"comprehensively":[101],"utilize":[102],"cross-modal":[103],"during":[106],"feature":[107],"extraction.":[108],"Fourthly,":[109],"source":[111],"domain":[112,115],"classifier":[113],"discriminator":[116],"employed":[118],"capture":[120],"modality-invariant":[121],"across":[123],"different":[124],"modalities.":[125],"Finally,":[126],"spatial":[128],"attention":[129,134],"aggregation":[130],"properties":[131],"graph":[133],"networks":[135],"leveraged":[137],"fuse":[139],"features,":[141],"thereby":[142],"contextual":[145],"information.":[147],"Experimental":[148],"results":[149],"real-world":[151],"datasets":[155],"from":[156],"domestic":[157],"railway":[158],"lines":[159],"demonstrate":[160],"that":[161],"proposed":[163],"method":[164],"achieves":[165],"95%":[166],"accuracy,":[168],"confirming":[169],"its":[170],"effectiveness":[171],"detection.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
