{"id":"https://openalex.org/W7154575024","doi":"https://doi.org/10.48550/arxiv.2604.13465","title":"Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding","display_name":"Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154575024","doi":"https://doi.org/10.48550/arxiv.2604.13465"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.13465","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040446901","display_name":"Ahmadreza Eslaminia","orcid":"https://orcid.org/0000-0002-9538-3328"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Eslaminia, Ahmadreza","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063766188","display_name":"Kuan-Chieh Lu","orcid":"https://orcid.org/0000-0002-1262-5054"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Kuan-Chieh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074224097","display_name":"Klara Nahrstedt","orcid":"https://orcid.org/0000-0001-6813-3043"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nahrstedt, Klara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059084183","display_name":"Chenhui Shao","orcid":"https://orcid.org/0000-0002-3299-2222"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Chenhui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040446901"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.49900001287460327,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.49900001287460327,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10723","display_name":"Advanced Welding Techniques Analysis","score":0.2653999924659729,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.047200001776218414,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.6736999750137329},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5253999829292297},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5042999982833862},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.4876999855041504},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.48660001158714294},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.37209999561309814},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3564000129699707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34299999475479126}],"concepts":[{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.6736999750137329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6175000071525574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5306000113487244},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5253999829292297},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.48660001158714294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4131999909877777},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.37209999561309814},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.31779998540878296},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3019999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30149999260902405},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.26109999418258667},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.13465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ultrasonic":[0],"metal":[1],"welding":[2],"(UMW)":[3],"is":[4,11,251],"widely":[5],"used":[6],"in":[7,48,181,238],"industrial":[8],"applications":[9],"but":[10],"sensitive":[12],"to":[13,24,53,133,253],"tool":[14],"wear,":[15],"surface":[16],"contamination,":[17],"and":[18,28,76,94,226,250],"material":[19],"variability,":[20],"which":[21,129],"can":[22],"lead":[23],"unexpected":[25],"process":[26,57,243],"faults":[27,83],"unsatisfactory":[29],"weld":[30],"quality.":[31],"Conventional":[32],"monitoring":[33,69,221,240],"systems":[34],"typically":[35],"rely":[36],"on":[37],"supervised":[38],"learning":[39,79,117,237],"models":[40],"that":[41,71,119,174,215],"assume":[42],"all":[43],"fault":[44,74,106,136,184,197],"types":[45,107,137],"are":[46,84,108],"known":[47,191],"advance,":[49],"limiting":[50],"their":[51],"ability":[52],"handle":[54],"previously":[55],"unseen":[56,183],"faults.":[58],"To":[59,144],"address":[60],"this":[61,63],"challenge,":[62],"paper":[64],"proposes":[65],"an":[66],"adaptive":[67,220],"condition":[68,239],"approach":[70,218,230],"enables":[72,130,219],"unknown":[73,105,159],"detection":[75],"few-shot":[77],"continual":[78,116,236],"for":[80,235],"UMW.":[81],"Unknown":[82],"detected":[85],"by":[86],"analyzing":[87],"hidden-layer":[88],"representations":[89],"of":[90,126,141,190],"a":[91,96,115,154,169,195,232],"multilayer":[92],"perceptron":[93],"leveraging":[95],"statistical":[97],"thresholding":[98],"strategy.":[99],"Once":[100],"detected,":[101],"the":[102,111,123,127,131,146,175,204,216],"samples":[103],"from":[104],"incorporated":[109],"into":[110],"existing":[112,142],"model":[113,132,206],"through":[114],"procedure":[118],"selectively":[120],"updates":[121],"only":[122,200],"final":[124],"layers":[125],"network,":[128],"recognize":[134],"new":[135,196,242],"while":[138,186],"preserving":[139],"knowledge":[140],"classes.":[143,192],"accelerate":[145],"labeling":[147,164],"process,":[148],"cosine":[149],"similarity":[150],"transformation":[151],"combined":[152],"with":[153,222],"clustering":[155],"algorithm":[156],"groups":[157],"similar":[158],"samples,":[160,203],"thereby":[161],"reducing":[162],"manual":[163],"effort.":[165],"Experimental":[166],"results":[167,213],"using":[168,199],"multi-sensor":[170],"UMW":[171],"dataset":[172],"demonstrate":[173,214],"proposed":[176,217,229],"method":[177],"achieves":[178,207],"96%":[179],"accuracy":[180],"detecting":[182],"conditions":[185,244],"maintaining":[187],"reliable":[188],"classification":[189,210],"After":[193],"incorporating":[194],"type":[198],"five":[201],"labeled":[202],"updated":[205],"98%":[208],"testing":[209],"accuracy.":[211],"These":[212],"minimal":[223],"retraining":[224],"cost":[225],"time.":[227],"The":[228],"provides":[231],"scalable":[233],"solution":[234],"where":[241],"may":[245],"constantly":[246],"emerge":[247],"over":[248],"time":[249],"extensible":[252],"other":[254],"manufacturing":[255],"processes.":[256]},"counts_by_year":[],"updated_date":"2026-04-17T06:04:52.305304","created_date":"2026-04-17T00:00:00"}
