{"id":"https://openalex.org/W4415310151","doi":"https://doi.org/10.48550/arxiv.2507.13022","title":"Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers","display_name":"Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers","publication_year":2025,"publication_date":"2025-07-17","ids":{"openalex":"https://openalex.org/W4415310151","doi":"https://doi.org/10.48550/arxiv.2507.13022"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.13022","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.13022","pdf_url":"https://arxiv.org/pdf/2507.13022","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.13022","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015483903","display_name":"Luis Basora","orcid":"https://orcid.org/0000-0002-5651-4663"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Basora, Luis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120047588","display_name":"Louison Bocquet-Nouaille","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bocquet-Nouaille, Louison","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086934420","display_name":"Elinirina I. Robinson","orcid":"https://orcid.org/0000-0003-2619-4061"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robinson, Elinirina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013120840","display_name":"Serge Le Gonidec","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gonidec, Serge Le","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015483903"],"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":0.9904999732971191,"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":0.9904999732971191,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9793999791145325,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T14470","display_name":"Advanced Data Processing Techniques","score":0.9674000144004822,"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/autoencoder","display_name":"Autoencoder","score":0.8314999938011169},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.65420001745224},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6507999897003174},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5099999904632568},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.489300012588501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48510000109672546},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.41850000619888306},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4034000039100647},{"id":"https://openalex.org/keywords/control-limits","display_name":"Control limits","score":0.4025000035762787}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8314999938011169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6633999943733215},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.65420001745224},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6507999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5511999726295471},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C166623804","wikidata":"https://www.wikidata.org/wiki/Q5165860","display_name":"Control limits","level":4,"score":0.4025000035762787},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.29660001397132874},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2903999984264374},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.13022","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.13022","pdf_url":"https://arxiv.org/pdf/2507.13022","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.13022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.13022","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.13022","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.13022","pdf_url":"https://arxiv.org/pdf/2507.13022","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,4,8,30,35,42,107,163,220],"context":[2],"of":[3,11,53],"health":[5],"monitoring":[6],"for":[7,29,61,224],"next":[9],"generation":[10],"reusable":[12],"space":[13],"launchers,":[14],"we":[15],"outline":[16],"a":[17,50,77,99,102,203],"first":[18,205],"step":[19],"toward":[20],"developing":[21],"an":[22],"onboard":[23],"fault":[24,174],"detection":[25,91],"and":[26,67,92,101,110,166,182,192],"diagnostic":[27],"capability":[28],"electrical":[31],"system":[32],"that":[33,124,199,217],"controls":[34],"engine":[36],"valves.":[37],"Unlike":[38],"existing":[39],"approaches":[40],"in":[41,173],"literature,":[43],"our":[44,200],"solution":[45,73,201],"is":[46,74,141,179,202],"designed":[47],"to":[48,81,121,143,161,169,215],"meet":[49],"broader":[51],"range":[52],"key":[54],"requirements.":[55],"This":[56],"includes":[57],"estimating":[58],"confidence":[59,129],"levels":[60],"predictions,":[62],"detecting":[63],"out-of-distribution":[64],"(OOD)":[65],"cases,":[66],"controlling":[68],"false":[69,164],"alarms.":[70],"The":[71,113,176,196],"proposed":[72,177],"based":[75,135],"on":[76,106,136,186],"temporal":[78],"convolutional":[79],"autoencoder":[80,108],"automatically":[82],"extract":[83],"low-dimensional":[84],"features":[85],"from":[86],"raw":[87],"sensor":[88],"data.":[89,146],"Fault":[90],"diagnosis":[93],"are":[94,115],"respectively":[95],"carried":[96],"out":[97],"using":[98],"binary":[100],"multiclass":[103],"classifier":[104],"trained":[105],"latent":[109],"residual":[111],"spaces.":[112],"classifiers":[114],"histogram-based":[116],"gradient":[117],"boosting":[118],"models":[119],"calibrated":[120],"output":[122],"probabilities":[123],"can":[125],"be":[126,213],"interpreted":[127],"as":[128,155],"levels.":[130],"A":[131],"relatively":[132],"simple":[133,150],"technique,":[134],"inductive":[137],"conformal":[138],"anomaly":[139],"detection,":[140],"used":[142],"identify":[144],"OOD":[145],"We":[147],"leverage":[148],"other":[149],"yet":[151],"effective":[152],"techniques,":[153],"such":[154],"cumulative":[156],"sum":[157],"control":[158],"chart":[159],"(CUSUM)":[160],"limit":[162],"alarms,":[165],"threshold":[167],"moving":[168],"address":[170],"class":[171],"imbalance":[172],"detection.":[175],"framework":[178],"highly":[180],"configurable":[181],"has":[183],"been":[184],"evaluated":[185],"simulated":[187],"data,":[188],"covering":[189],"both":[190],"nominal":[191],"anomalous":[193],"operational":[194,225],"scenarios.":[195],"results":[197],"indicate":[198],"promising":[204],"step,":[206],"though":[207],"testing":[208],"with":[209],"real":[210],"data":[211],"will":[212],"necessary":[214],"ensure":[216],"it":[218],"achieves":[219],"required":[221],"maturity":[222],"level":[223],"use.":[226]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-18T00:00:00"}