{"id":"https://openalex.org/W2611805286","doi":"https://doi.org/10.1080/08839514.2017.1315502","title":"An SVM\u2014ANN Hybrid Classifier for Diagnosis of Gear Fault","display_name":"An SVM\u2014ANN Hybrid Classifier for Diagnosis of Gear Fault","publication_year":2017,"publication_date":"2017-05-02","ids":{"openalex":"https://openalex.org/W2611805286","doi":"https://doi.org/10.1080/08839514.2017.1315502","mag":"2611805286"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2017.1315502","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2017.1315502","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5062865229","display_name":"Sunil Tyagi","orcid":"https://orcid.org/0000-0001-5897-7955"},"institutions":[{"id":"https://openalex.org/I156406944","display_name":"Defence Institute of Advanced Technology","ror":"https://ror.org/05qpbfx18","country_code":"IN","type":"education","lineage":["https://openalex.org/I156406944"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sunil Tyagi","raw_affiliation_strings":["Department of Mechanical Engineering, Defense Institute of Advance Technology, Girinagar, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Defense Institute of Advance Technology, Girinagar, Pune, India","institution_ids":["https://openalex.org/I156406944"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023484456","display_name":"S.K. Panigrahi","orcid":"https://orcid.org/0000-0001-6624-0988"},"institutions":[{"id":"https://openalex.org/I156406944","display_name":"Defence Institute of Advanced Technology","ror":"https://ror.org/05qpbfx18","country_code":"IN","type":"education","lineage":["https://openalex.org/I156406944"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"SK Panigrahi","raw_affiliation_strings":["Department of Mechanical Engineering, Defense Institute of Advance Technology, Girinagar, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Defense Institute of Advance Technology, Girinagar, Pune, India","institution_ids":["https://openalex.org/I156406944"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023484456"],"corresponding_institution_ids":["https://openalex.org/I156406944"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":1.6326,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.84245255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9970999956130981,"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":0.9970999956130981,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.991100013256073,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9907000064849854,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.8086724877357483},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7585806846618652},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7431142926216125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7140673995018005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6420125961303711},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5782579183578491},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.5260258316993713},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4728415608406067},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.43621882796287537},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4326504170894623},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08846235275268555},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.06706079840660095}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8086724877357483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7585806846618652},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7431142926216125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7140673995018005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6420125961303711},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5782579183578491},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.5260258316993713},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4728415608406067},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.43621882796287537},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4326504170894623},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08846235275268555},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.06706079840660095},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2017.1315502","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2017.1315502","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e82de7452f88414d89725d03a32e86cc","is_oa":false,"landing_page_url":"https://doaj.org/article/e82de7452f88414d89725d03a32e86cc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 31, Iss 3, Pp 209-231 (2017)","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W618561231","https://openalex.org/W1525501921","https://openalex.org/W1606846851","https://openalex.org/W1966237354","https://openalex.org/W1980811222","https://openalex.org/W1989226616","https://openalex.org/W2002016471","https://openalex.org/W2005934359","https://openalex.org/W2011534991","https://openalex.org/W2024968541","https://openalex.org/W2034790739","https://openalex.org/W2037411704","https://openalex.org/W2040708543","https://openalex.org/W2041124359","https://openalex.org/W2059404375","https://openalex.org/W2074724805","https://openalex.org/W2087299198","https://openalex.org/W2114160202","https://openalex.org/W2132984323","https://openalex.org/W2134249892","https://openalex.org/W2148603752","https://openalex.org/W2149854044","https://openalex.org/W2150313473","https://openalex.org/W2158442843","https://openalex.org/W2163534527","https://openalex.org/W2186535340","https://openalex.org/W2326584678","https://openalex.org/W2396076036","https://openalex.org/W2497275036","https://openalex.org/W2622332645","https://openalex.org/W4254721730","https://openalex.org/W4301052031"],"related_works":["https://openalex.org/W2384390720","https://openalex.org/W2297694731","https://openalex.org/W4253128984","https://openalex.org/W2539940072","https://openalex.org/W2392574027","https://openalex.org/W206493657","https://openalex.org/W1964081096","https://openalex.org/W2122277321","https://openalex.org/W2554106811","https://openalex.org/W1483596504"],"abstract_inverted_index":{"A":[0],"hybrid":[1,49,88,98,115,140],"classifier":[2,89,100,116],"obtained":[3,27,74],"by":[4,131],"hybridizing":[5],"Support":[6],"Vector":[7],"Machines":[8],"(SVM)":[9],"and":[10,40,65,70,95,109],"Artificial":[11],"Neural":[12],"Network":[13],"(ANN)":[14],"classifiers":[15],"is":[16,141],"presented":[17],"here":[18],"for":[19],"diagnosis":[20],"of":[21,31,107,113,137],"gear":[22,93],"faults.":[23],"The":[24,111],"distinctive":[25],"features":[26],"from":[28,75],"vibration":[29,52,127],"signals":[30,53,128],"a":[32],"running":[33],"gearbox,":[34],"which":[35],"was":[36,84,119,123],"operated":[37],"in":[38,56,62,66],"normal":[39],"fault-induced":[41],"conditions,":[42],"were":[43,54,73],"used":[44],"to":[45],"feed":[46],"the":[47,80,97,114,138],"SVM-ANN":[48,87,99,139],"classifier.":[50],"Time-domain":[51],"divided":[55],"segments.":[57,77],"Features":[58],"such":[59],"as":[60],"peaks":[61],"time":[63],"domain":[64],"spectrum,":[67],"central":[68],"moments,":[69],"standard":[71,105],"deviations":[72],"signal":[76],"Based":[78],"on":[79],"experimental":[81],"results,":[82],"it":[83],"shown":[85,124],"that":[86,96,125],"can":[90],"successfully":[91],"identify":[92],"condition":[94],"performs":[101],"much":[102],"better":[103],"than":[104],"versions":[106],"ANNs":[108],"SVM.":[110],"effectiveness":[112],"under":[117],"noise":[118],"also":[120],"investigated.":[121],"It":[122],"if":[126],"are":[129],"preprocessed":[130],"Discrete":[132],"Wavelet":[133],"Transform":[134],"(DWT),":[135],"efficacy":[136],"significantly":[142],"enhanced.":[143]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2025-10-10T00:00:00"}
