{"id":"https://openalex.org/W7107967794","doi":"https://doi.org/10.1007/s10791-025-09823-8","title":"Automated vehicle fault diagnosis and report generation using hybrid machine learning with multi-step RAG approach","display_name":"Automated vehicle fault diagnosis and report generation using hybrid machine learning with multi-step RAG approach","publication_year":2025,"publication_date":"2025-11-29","ids":{"openalex":"https://openalex.org/W7107967794","doi":"https://doi.org/10.1007/s10791-025-09823-8"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-025-09823-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09823-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09823-8.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09823-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yashashree Mahale","orcid":null},"institutions":[{"id":"https://openalex.org/I244572783","display_name":"Symbiosis International University","ror":"https://ror.org/005r2ww51","country_code":"IN","type":"education","lineage":["https://openalex.org/I244572783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yashashree Mahale","raw_affiliation_strings":["Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India","institution_ids":["https://openalex.org/I244572783"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shrikrishna Kolhar","orcid":null},"institutions":[{"id":"https://openalex.org/I244572783","display_name":"Symbiosis International University","ror":"https://ror.org/005r2ww51","country_code":"IN","type":"education","lineage":["https://openalex.org/I244572783"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shrikrishna Kolhar","raw_affiliation_strings":["Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India","institution_ids":["https://openalex.org/I244572783"]}]},{"author_position":"last","author":{"id":null,"display_name":"Anjali S. More","orcid":null},"institutions":[{"id":"https://openalex.org/I244572783","display_name":"Symbiosis International University","ror":"https://ror.org/005r2ww51","country_code":"IN","type":"education","lineage":["https://openalex.org/I244572783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anjali S. More","raw_affiliation_strings":["Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, 412115, Maharashtra, India","institution_ids":["https://openalex.org/I244572783"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I244572783"],"apc_list":null,"apc_paid":null,"fwci":0.7779,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.78926251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"28","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.19329999387264252,"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":0.19329999387264252,"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/T10028","display_name":"Topic Modeling","score":0.07460000365972519,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06400000303983688,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8633999824523926},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.5827999711036682},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5442000031471252},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.44909998774528503},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.36390000581741333},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3555999994277954},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35530000925064087}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8633999824523926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6553999781608582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6119999885559082},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5827999711036682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.54830002784729},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5442000031471252},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3285999894142151},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C160713754","wikidata":"https://www.wikidata.org/wiki/Q1389965","display_name":"Maintainability","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C107094494","wikidata":"https://www.wikidata.org/wiki/Q428453","display_name":"Fault tree analysis","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C111065885","wikidata":"https://www.wikidata.org/wiki/Q1189053","display_name":"Fuzz testing","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10791-025-09823-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09823-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09823-8.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:68bc78139d6145d6ad5a4994c598bf11","is_oa":true,"landing_page_url":"https://doaj.org/article/68bc78139d6145d6ad5a4994c598bf11","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Computing, Vol 28, Iss 1, Pp 1-21 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10791-025-09823-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09823-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09823-8.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6518969535827637,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7107967794.pdf","grobid_xml":"https://content.openalex.org/works/W7107967794.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W3034357049","https://openalex.org/W3088024865","https://openalex.org/W3133798627","https://openalex.org/W3198942453","https://openalex.org/W4205603456","https://openalex.org/W4292972564","https://openalex.org/W4296129772","https://openalex.org/W4319663621","https://openalex.org/W4377090333","https://openalex.org/W4383466009","https://openalex.org/W4384693822","https://openalex.org/W4387342650","https://openalex.org/W4388205701","https://openalex.org/W4390879292","https://openalex.org/W4393947202","https://openalex.org/W4394797211","https://openalex.org/W4396597242","https://openalex.org/W4396920486","https://openalex.org/W4399125578","https://openalex.org/W4400232509","https://openalex.org/W4400477691","https://openalex.org/W4400645432","https://openalex.org/W4400649839","https://openalex.org/W4401538712","https://openalex.org/W4403520158","https://openalex.org/W4405232509","https://openalex.org/W4405754595","https://openalex.org/W4407030096","https://openalex.org/W4408665242","https://openalex.org/W4409379112","https://openalex.org/W4410516897","https://openalex.org/W4412480620","https://openalex.org/W4414334072"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"vehicle":[1,78],"diagnostics":[2,213],"is":[3,60,137],"challenging":[4],"in":[5,85,181,211],"the":[6,74,200],"automotive":[7,212],"industry":[8],"due":[9],"to":[10,48,62,105,116,147],"complex":[11],"failure":[12,95],"patterns":[13],"and":[14,32,52,68,109,150,166,195,214],"fragmented":[15],"information.":[16],"While":[17],"AI-based":[18],"predictive":[19],"maintenance":[20],"systems":[21],"can":[22],"forecast":[23],"failures":[24],"using":[25,97,156],"historical":[26],"data,":[27],"they":[28],"often":[29],"lack":[30],"interpretability":[31],"overlook":[33],"valuable":[34],"technical":[35,123,185],"documentation.":[36],"This":[37,187],"study":[38],"proposes":[39],"a":[40,86,98,112,130,143,204],"holistic":[41],"diagnostic":[42,79,88],"framework":[43,202],"that":[44,90,199],"utilizes":[45],"generative":[46,101],"AI":[47,210],"improve":[49],"fault":[50,70],"prediction":[51],"explainability.":[53],"A":[54],"novel":[55],"Retrieval-Augmented":[56],"Generation":[57],"(RAG)":[58],"architecture":[59,115],"introduced":[61],"extract":[63],"knowledge":[64],"from":[65],"unstructured":[66,122],"manuals":[67,124],"structured":[69,118],"code":[71],"databases,":[72],"enabling":[73],"generation":[75],"of":[76,162,179],"comprehensive":[77],"reports.":[80],"The":[81],"core":[82],"innovation":[83],"lies":[84],"hybrid":[87,144],"pipeline":[89],"(i)":[91],"generates":[92],"realistic":[93],"synthetic":[94],"cases":[96],"conditional":[99],"tabular":[100],"adversarial":[102],"network":[103],"(CTGAN)":[104],"mitigate":[106],"class":[107],"imbalance,":[108],"(ii)":[110],"employs":[111],"multi-source":[113],"RAG":[114],"unify":[117],"fault-code":[119],"databases":[120],"with":[121,133],"for":[125,139,164],"context-aware":[126],"diagnostics.":[127],"CTGAN":[128],"achieved":[129],"96%":[131],"correlation":[132],"real":[134],"data.":[135],"GPT-4o":[136],"employed":[138],"report":[140],"generation,":[141],"utilizing":[142],"prompting":[145],"technique":[146],"enhance":[148],"accuracy":[149,171],"coherence.":[151],"Report":[152],"quality":[153],"was":[154],"evaluated":[155],"multiple":[157],"LLMs,":[158],"yielding":[159],"average":[160],"scores":[161,172],"85%":[163],"readability":[165],"contextual":[167],"relevance.":[168],"However,":[169],"factual":[170],"were":[173],"comparatively":[174],"lower,":[175],"reflecting":[176],"current":[177],"limitations":[178],"LLMs":[180],"maintaining":[182],"consistency":[183],"across":[184],"domains.":[186],"highlights":[188],"an":[189],"important":[190],"research":[191],"direction\u2013improving":[192],"domain":[193],"alignment":[194],"verification":[196],"mechanisms\u2013while":[197],"demonstrating":[198],"proposed":[201],"remains":[203],"strong":[205],"step":[206],"toward":[207],"integrating":[208],"explainable":[209],"fleet":[215],"management.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-12-01T00:00:00"}
