{"id":"https://openalex.org/W7155401795","doi":"https://doi.org/10.48550/arxiv.2604.20735","title":"Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health","display_name":"Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155401795","doi":"https://doi.org/10.48550/arxiv.2604.20735"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20735","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20735","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.20735","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134436921","display_name":"Peter Collett","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Collett, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066901898","display_name":"A. Stasik","orcid":"https://orcid.org/0000-0003-1646-2472"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stasik, Alexander Johannes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027863128","display_name":"Simone Casolo","orcid":"https://orcid.org/0000-0003-2945-8931"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Casolo, Simone","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5091724256","display_name":"Signe Riemer\u2013S\u00f8rensen","orcid":"https://orcid.org/0000-0002-5308-7651"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riemer-S\u00f8rensen, Signe","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/T11948","display_name":"Machine Learning in Materials Science","score":0.2599000036716461,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.2599000036716461,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.1396999955177307,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.1362999975681305,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6086999773979187},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4970000088214874},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49540001153945923},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4884999990463257},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.46239998936653137},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4449000060558319},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44279998540878296},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43549999594688416},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.42730000615119934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.666100025177002},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6086999773979187},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4970000088214874},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.46239998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.447299987077713},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34860000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34630000591278076},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3325999975204468},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30709999799728394},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20735","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20735","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.20735","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20735","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4508117139339447}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"condition":[1],"monitoring":[2],"of":[3,93,144],"industrial":[4],"equipment":[5],"requires":[6],"inferring":[7],"latent":[8],"degradation":[9,94],"parameters":[10],"from":[11,85],"indirect":[12],"sensor":[13],"measurements":[14],"under":[15],"uncertainty.":[16],"While":[17],"traditional":[18,139],"Bayesian":[19],"methods":[20],"like":[21],"Markov":[22],"Chain":[23],"Monte":[24],"Carlo":[25],"(MCMC)":[26],"provide":[27],"rigorous":[28],"uncertainty":[29,127],"quantification,":[30,128],"their":[31],"heavy":[32],"computational":[33],"bottlenecks":[34],"render":[35],"them":[36],"impractical":[37],"for":[38,159],"real-time":[39,157],"process":[40],"control.":[41],"To":[42],"overcome":[43],"this":[44,98],"limitation,":[45],"we":[46],"propose":[47],"an":[48,101],"AI-driven":[49],"framework":[50,99],"utilizing":[51],"Simulation-Based":[52],"Inference":[53],"(SBI)":[54],"powered":[55],"by":[56,133],"amortized":[57,142],"neural":[58,71,146],"posterior":[59,91],"estimation":[60],"to":[61,88,138],"diagnose":[62],"complex":[63,168],"failure":[64],"modes":[65],"in":[66,167],"heat":[67],"exchangers.":[68],"By":[69],"training":[70],"density":[72],"estimators":[73],"on":[74],"a":[75,81,134,154],"simulated":[76],"dataset,":[77],"our":[78],"approach":[79],"learns":[80],"direct,":[82],"likelihood-free":[83],"mapping":[84],"thermal-fluid":[86],"observations":[87],"the":[89,145],"full":[90],"distribution":[92],"parameters.":[95],"We":[96],"benchmark":[97],"against":[100],"MCMC":[102],"baseline":[103],"across":[104],"various":[105],"synthetic":[106],"fouling":[107],"and":[108,125,163],"leakage":[109],"scenarios,":[110],"including":[111],"challenging":[112],"low-probability,":[113],"sparse-event":[114],"failures.":[115],"The":[116,141],"results":[117],"show":[118],"that":[119],"SBI":[120,152],"achieves":[121],"comparable":[122],"diagnostic":[123],"accuracy":[124],"reliable":[126],"while":[129],"accelerating":[130],"inference":[131],"time":[132],"factor":[135],"of82$\\times$":[136],"compared":[137],"sampling.":[140],"nature":[143],"network":[147],"enables":[148],"near-instantaneous":[149],"inference,":[150],"establishing":[151],"as":[153],"highly":[155],"scalable,":[156],"alternative":[158],"probabilistic":[160],"fault":[161],"diagnosis":[162],"digital":[164],"twin":[165],"realization":[166],"engineering":[169],"systems.":[170]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-24T00:00:00"}