{"id":"https://openalex.org/W4225995069","doi":"https://doi.org/10.1109/tai.2022.3165137","title":"An Extreme Gradient Boosting Aided Fault Diagnosis Approach: A Case Study of Fuse Test Bench","display_name":"An Extreme Gradient Boosting Aided Fault Diagnosis Approach: A Case Study of Fuse Test Bench","publication_year":2022,"publication_date":"2022-04-06","ids":{"openalex":"https://openalex.org/W4225995069","doi":"https://doi.org/10.1109/tai.2022.3165137"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2022.3165137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3165137","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Transactions on Artificial Intelligence","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/A5019632486","display_name":"Muhammad Gibran Alfarizi","orcid":"https://orcid.org/0000-0002-6373-279X"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Muhammad Gibran Alfarizi","raw_affiliation_strings":["Norwegian University of Science and Technology, Trondheim, Norway"],"raw_orcid":"https://orcid.org/0000-0002-6373-279X","affiliations":[{"raw_affiliation_string":"Norwegian University of Science and Technology, Trondheim, Norway","institution_ids":["https://openalex.org/I204778367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032650678","display_name":"J\u00f8rn Vatn","orcid":"https://orcid.org/0000-0002-7562-0672"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"J\u00f8rn Vatn","raw_affiliation_strings":["Norwegian University of Science and Technology, Trondheim, Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Norwegian University of Science and Technology, Trondheim, Norway","institution_ids":["https://openalex.org/I204778367"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069744156","display_name":"Shen Yin","orcid":"https://orcid.org/0000-0002-3802-9269"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Shen Yin","raw_affiliation_strings":["Norwegian University of Science and Technology, Trondheim, Norway"],"raw_orcid":"https://orcid.org/0000-0002-3802-9269","affiliations":[{"raw_affiliation_string":"Norwegian University of Science and Technology, Trondheim, Norway","institution_ids":["https://openalex.org/I204778367"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204778367"],"apc_list":null,"apc_paid":null,"fwci":2.9127,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.90749962,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"4","issue":"4","first_page":"661","last_page":"668"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9976000189781189,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9976000189781189,"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/T11357","display_name":"Risk and Safety Analysis","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9829999804496765,"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/fuse","display_name":"Fuse (electrical)","score":0.7816758751869202},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.5614233016967773},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5477933883666992},{"id":"https://openalex.org/keywords/root-cause","display_name":"Root cause","score":0.5252577662467957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4906543791294098},{"id":"https://openalex.org/keywords/test-bench","display_name":"Test bench","score":0.4795491397380829},{"id":"https://openalex.org/keywords/root-cause-analysis","display_name":"Root cause analysis","score":0.4608682096004486},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4141131043434143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35325998067855835},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3070574402809143},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.1741124987602234}],"concepts":[{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7816758751869202},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.5614233016967773},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5477933883666992},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.5252577662467957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4906543791294098},{"id":"https://openalex.org/C2776266606","wikidata":"https://www.wikidata.org/wiki/Q476482","display_name":"Test bench","level":2,"score":0.4795491397380829},{"id":"https://openalex.org/C130963320","wikidata":"https://www.wikidata.org/wiki/Q1401207","display_name":"Root cause analysis","level":2,"score":0.4608682096004486},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4141131043434143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35325998067855835},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3070574402809143},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.1741124987602234},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2022.3165137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3165137","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Transactions on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W108857541","https://openalex.org/W604737461","https://openalex.org/W1535602073","https://openalex.org/W1597576211","https://openalex.org/W1678356000","https://openalex.org/W1984672166","https://openalex.org/W2072857564","https://openalex.org/W2143585755","https://openalex.org/W2158958729","https://openalex.org/W2175473154","https://openalex.org/W2193145675","https://openalex.org/W2295598076","https://openalex.org/W2494112937","https://openalex.org/W2530806392","https://openalex.org/W2594332903","https://openalex.org/W2768348081","https://openalex.org/W2770049107","https://openalex.org/W2772386856","https://openalex.org/W2794381451","https://openalex.org/W2795516651","https://openalex.org/W2803202359","https://openalex.org/W2804601703","https://openalex.org/W2888309058","https://openalex.org/W2889395339","https://openalex.org/W2911964244","https://openalex.org/W2950150695","https://openalex.org/W2963037989","https://openalex.org/W2987147016","https://openalex.org/W3091965860","https://openalex.org/W3094872754","https://openalex.org/W3106250896","https://openalex.org/W3137165991","https://openalex.org/W3156231038","https://openalex.org/W3196675449","https://openalex.org/W3199102655","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W3005087358","https://openalex.org/W2377602682","https://openalex.org/W2141950192","https://openalex.org/W141106878","https://openalex.org/W1988621644","https://openalex.org/W2210812206","https://openalex.org/W593352287","https://openalex.org/W2346194807","https://openalex.org/W4226007729"],"abstract_inverted_index":{"The":[0,99],"health":[1],"status":[2],"of":[3,16,33,53,116,138],"a":[4,41],"fuse":[5,92],"test":[6,93],"bench":[7,94],"is":[8,37,103,127],"essential":[9],"to":[10,12,26,39,45,55,95],"monitor":[11],"ensure":[13],"quality":[14,32],"control":[15],"the":[17,30,50,107,117,135,139],"fuse.":[18],"A":[19],"system":[20,44,63,83,102,120,141],"failure":[21],"during":[22],"operation":[23,57],"will":[24],"lead":[25],"significant":[27],"impacts":[28],"on":[29,85],"final":[31],"fuses.":[34],"Thus,":[35],"it":[36],"important":[38],"have":[40,65],"fault":[42,61,81,118,145],"diagnosis":[43,62,69,82,101,119],"detect,":[46],"classify,":[47],"and":[48,71],"identify":[49],"root":[51,73],"causes":[52],"faults":[54],"prevent":[56],"failure.":[58],"An":[59],"effective":[60],"should":[64],"high":[66],"accuracy,":[67],"fast":[68],"time,":[70],"interpretable":[72],"cause":[74],"analysis.":[75],"This":[76],"article":[77],"proposes":[78],"an":[79,90],"integrated":[80],"based":[84],"extreme":[86],"gradient":[87],"boosting":[88],"for":[89],"automated":[91],"solve":[96],"those":[97],"challenges.":[98],"proposed":[100,140],"then":[104],"validated":[105],"using":[106],"dataset":[108],"from":[109],"PHM":[110],"2021":[111],"Data":[112],"Challenge.":[113],"Performance":[114],"comparison":[115],"with":[121],"other":[122],"standard":[123,144],"approaches":[124],"in":[125],"practice":[126],"also":[128],"carried":[129],"out.":[130],"Experimental":[131],"results":[132],"show":[133],"that":[134],"diagnostic":[136,146],"accuracy":[137],"outperforms":[142],"several":[143],"approaches.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
