{"id":"https://openalex.org/W7154382336","doi":"https://doi.org/10.48550/arxiv.2604.09932","title":"A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems","display_name":"A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154382336","doi":"https://doi.org/10.48550/arxiv.2604.09932"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09932","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.09932","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010030583","display_name":"Maryam Ahang","orcid":"https://orcid.org/0000-0001-5580-3693"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahang, Maryam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023240463","display_name":"Todd Charter","orcid":"https://orcid.org/0000-0001-5982-255X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Charter, Todd","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010044195","display_name":"Masoud Jalayer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jalayer, Masoud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058540009","display_name":"Homayoun Najjaran","orcid":"https://orcid.org/0000-0002-3550-225X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Najjaran, Homayoun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10876","display_name":"Fault Detection and Control Systems","score":0.7685999870300293,"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":0.7685999870300293,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.041099999099969864,"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":0.03519999980926514,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.70169997215271},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6650999784469604},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6233000159263611},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.49779999256134033},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.48510000109672546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43880000710487366},{"id":"https://openalex.org/keywords/hybrid-system","display_name":"Hybrid system","score":0.4348999857902527},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4189999997615814},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.375}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.70169997215271},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6650999784469604},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6233000159263611},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6028000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.567300021648407},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5586000084877014},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.49779999256134033},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.375},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35420000553131104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C2776907094","wikidata":"https://www.wikidata.org/wiki/Q1043452","display_name":"Condition-based maintenance","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2736999988555908},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09932","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.09932","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09932","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":"Preprint"},"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":{"Hybrid":[0],"approaches":[1,92],"that":[2,28,57,160,179],"combine":[3],"data-driven":[4],"learning":[5,77,110,187],"with":[6,62,127],"physics-based":[7],"insight":[8],"have":[9],"shown":[10],"promise":[11],"for":[12],"improving":[13],"the":[14,59,87,94,128,137],"reliability":[15,198],"of":[16,93],"industrial":[17,201],"condition":[18,25,95],"monitoring.":[19],"This":[20],"work":[21],"develops":[22],"a":[23,53,70,101,133],"hybrid":[24,45,91,161],"monitoring":[26,96],"framework":[27,97],"integrates":[29],"primary":[30],"sensor":[31],"measurements,":[32],"lagged":[33],"temporal":[34,65,183],"features,":[35],"and":[36,64,112,117,154,168,185,196],"physics-informed":[37,181],"residuals":[38],"derived":[39],"from":[40],"nominal":[41],"surrogate":[42],"models.":[43],"Two":[44],"integration":[46,162],"strategies":[47],"are":[48,84,98],"examined.":[49],"The":[50,67,157],"first":[51],"is":[52,69,147],"feature-level":[54,116],"fusion":[55],"approach":[56,73],"augments":[58],"input":[60],"space":[61],"residual":[63],"information.":[66],"second":[68],"model-level":[71,118,130],"ensemble":[72,113,131,186],"in":[74,199],"which":[75],"machine":[76,109],"classifiers":[78],"trained":[79],"on":[80,100],"different":[81],"feature":[82],"types":[83],"combined":[85,190],"at":[86,172],"decision":[88,197],"level.":[89],"Both":[90,115],"evaluated":[99],"continuous":[102],"stirred-tank":[103],"reactor":[104],"(CSTR)":[105],"benchmark":[106],"using":[107],"several":[108],"models":[111],"configurations.":[114],"hybridization":[119],"improve":[120,193],"diagnostic":[121],"accuracy":[122,195],"relative":[123],"to":[124,149,192],"single-source":[125],"baselines,":[126],"best":[129,138],"achieving":[132],"2.9\\%":[134],"improvement":[135],"over":[136],"baseline":[139],"ensemble.":[140],"To":[141],"assess":[142],"predictive":[143],"reliability,":[144],"conformal":[145],"prediction":[146,170],"applied":[148],"quantify":[150],"coverage,":[151],"prediction-set":[152],"size,":[153],"abstention":[155],"behavior.":[156],"results":[158],"show":[159],"enhances":[163],"uncertainty":[164],"management,":[165],"producing":[166],"smaller":[167],"well-calibrated":[169],"sets":[171],"matched":[173],"coverage":[174],"levels.":[175],"These":[176],"findings":[177],"demonstrate":[178],"lightweight":[180],"residuals,":[182],"augmentation,":[184],"can":[188],"be":[189],"effectively":[191],"both":[194],"nonlinear":[200],"systems.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-04-15T00:00:00"}