{"id":"https://openalex.org/W7127399559","doi":"https://doi.org/10.1109/jiot.2026.3660745","title":"Multimodal Feature Interactive Network for Machinery Fault Diagnosis Under Small Samples","display_name":"Multimodal Feature Interactive Network for Machinery Fault Diagnosis Under Small Samples","publication_year":2026,"publication_date":"2026-02-03","ids":{"openalex":"https://openalex.org/W7127399559","doi":"https://doi.org/10.1109/jiot.2026.3660745"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3660745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3660745","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103233049","display_name":"Ziyue Jiang","orcid":"https://orcid.org/0009-0006-0078-2315"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyue Jiang","raw_affiliation_strings":["School of Mechanical Engineering, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0006-0078-2315","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ran Li","orcid":"https://orcid.org/0009-0003-7575-0009"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Li","raw_affiliation_strings":["National Aerospace Intelligence Control Technology Laboratory, Beijing Aerospace Automatic Control Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-7575-0009","affiliations":[{"raw_affiliation_string":"National Aerospace Intelligence Control Technology Laboratory, Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jianbo Yu","orcid":"https://orcid.org/0000-0003-3204-2486"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbo Yu","raw_affiliation_strings":["School of Mechanical Engineering, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-3204-2486","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103233049"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2286975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"8","first_page":"16831","last_page":"16841"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.965499997138977,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.965499997138977,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.006899999920278788,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.003800000064074993,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/feature-extraction","display_name":"Feature extraction","score":0.6478999853134155},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6230000257492065},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.557699978351593},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5450000166893005},{"id":"https://openalex.org/keywords/test-bench","display_name":"Test bench","score":0.5117999911308289},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5084999799728394},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3677000105381012},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.3630000054836273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7721999883651733},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6478999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6230000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6115999817848206},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.557699978351593},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C2776266606","wikidata":"https://www.wikidata.org/wiki/Q476482","display_name":"Test bench","level":2,"score":0.5117999911308289},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.310699999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C17281054","wikidata":"https://www.wikidata.org/wiki/Q193466","display_name":"Rotor (electric)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3660745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3660745","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.685066819190979}],"awards":[{"id":"https://openalex.org/G1342925292","display_name":null,"funder_award_id":"92167107","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vibration":[0],"sensing":[1],"and":[2,22,125,143,160,183],"infrared":[3,23],"thermal":[4,24,108],"image":[5],"technology":[6],"have":[7],"been":[8],"widely":[9],"used":[10],"in":[11,76,103],"the":[12,33,38,134,177,188],"health":[13],"monitoring":[14],"of":[15,35,40,174,190],"machines.":[16],"Multimodal":[17],"fault":[18,54,165,171],"diagnosis":[19,55,166],"combining":[20],"vibration":[21],"data":[25,59,158],"has":[26,152],"shown":[27],"considerable":[28],"potential.":[29],"However,":[30],"due":[31],"to":[32,52,88,105],"heterogeneity":[34],"multimodal":[36,58,69,81,115,157,179],"data,":[37],"performance":[39,155],"some":[41],"models":[42],"will":[43],"significantly":[44],"decrease":[45],"under":[46,60,93,167],"small":[47,61,94,168],"samples.":[48,62,95,169],"It":[49,186],"is":[50,74,86,101],"challenging":[51],"implement":[53],"based":[56],"on":[57,137,156,176],"Thus,":[63],"a":[64,80,97,114,138,144,153],"new":[65],"DNN,":[66],"i.e.,":[67],"adaptive":[68,90,129],"feature":[70,82,91,116,161],"interactive":[71,83],"network":[72],"(ADMMFI),":[73],"proposed":[75,87,102],"this":[77],"study,":[78],"where":[79],"(MMFI)":[84,119],"module":[85,100,118],"perform":[89],"fusion":[92,130,159],"Firstly,":[96],"dimension":[98],"reshaping":[99],"ADMMFI":[104,151,175,191],"preserve":[106],"discriminative":[107],"features":[109,122],"during":[110],"modality":[111],"transformation.":[112],"Secondly,":[113],"interaction":[117],"dynamically":[120],"separates":[121],"into":[123],"private":[124],"shared":[126],"components,":[127],"enabling":[128],"across":[131],"modalities.":[132],"Finally,":[133],"experimental":[135],"results":[136],"rolling":[139],"bearing":[140],"test":[141,147],"bench":[142,148],"rotor":[145],"system":[146],"show":[149],"that":[150],"good":[154],"extraction":[162],"for":[163],"machinery":[164],"The":[170],"recognition":[172],"accuracy":[173],"two":[178],"datasets":[180],"was":[181],"79.17%":[182],"95.15%,":[184],"respectively.":[185],"demonstrates":[187],"effectiveness":[189],"compared":[192],"with":[193],"other":[194],"DNNs.":[195]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-02-04T00:00:00"}
